Publications

Publications Conference Papers

Extraction of Gene-Environment Interaction from the Biomedical Literature

Jinseon You, Jin-Woo Chung, Wonsuk Yang, and Jong C. Park
8th International Joint Conference on Natural Language Processing (IJCNLP 2017) (accepted)

Inferring Implicit Event Locations from Context with Distributional Similarities

Jin-Woo Chung, Wonsuk Yang, Jinseon You, and Jong C. Park
Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 979-985, Melbourne, Australia, August 19-25, 2017.
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Automatic event location extraction from text plays a crucial role in many applications such as infectious disease surveillance and natural disaster monitoring. The fundamental limitation of previous work such as SpaceEval is the limited scope of extraction, targeting only at locations that are explicitly stated in a syntactic structure. This leads to missing a lot of implicit information inferable from context in a document, which amounts to nearly 40% of the entire location information. To overcome this limitation for the first time, we present a system that infers the implicit event locations from a given document. Our system exploits distributional semantics, based on the hypothesis that if two events are described by similar expressions, it is likely that they occur in the same location. For example, if โ€œA bomb exploded causing 30 victimsโ€ and โ€œmany people died from terrorist attack in Bostonโ€ are reported in the same document, it is highly likely that the bomb exploded in Boston. Our system shows good performance of a 0.58 F1-score, where state-of-the-art classifiers for intra-sentential spatiotemporal relations achieve around 0.60 F1-scores.

Neural Theorem Prover with Word Embedding for Efficient Automatic Annotation

Wonsuk Yang, Hancheol Park, and Jong C. Park
Proceedings of the 28th Annual Conference on Human and Cognitive Language Technology (HCLT) pp. 79-84, Busan, Korea, October 07-08, 2016.
(selected as best paper)
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๋ณธ ์—ฐ๊ตฌ๋Š” ์ „๋ฌธ๊ธฐ๊ด€์—์„œ ์ƒ์‚ฐ๋˜๋Š” ๊ฒ€์ฆ๋œ ๋ฌธ์„œ๋ฅผ ์›น์ƒ์˜ ์ˆ˜๋งŽ์€ ๊ฒ€์ฆ๋˜์ง€ ์•Š์€ ๋ฌธ์„œ์— ์ž๋™ ์ฃผ์„ํ•˜์—ฌ ์‹  ๋ขฐ๋„ ํ–ฅ์ƒ ๋ฐ ์‹ฌํ™” ์ •๋ณด๋ฅผ ์ž๋™์œผ๋กœ ์ถ”๊ฐ€ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ™œ์šฉ ๊ฐ€๋Šฅ ํ•œ ์‹œ์Šคํ…œ์ธ ์ธ๊ณต ์‹ ๊ฒฝ ์ •๋ฆฌ ์ฆ๋ช…๊ณ„(neural theorem prover)๊ฐ€ ๋Œ€๊ทœ๋ชจ ๋ง๋ญ‰์น˜์— ์ ์šฉ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ทผ๋ณธ ์ ์ธ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‚ด๋ถ€ ์ˆœํ™˜ ๋ชจ๋“ˆ์„ ๋‹จ์–ด ์ž„๋ฒ ๋”ฉ ๋ชจ๋“ˆ๋กœ ๊ต์ฒดํ•˜์—ฌ ์žฌ๊ตฌ์ถ• ํ•˜์˜€๋‹ค. ํ•™์Šต ์‹œ๊ฐ„์˜ ํš๊ธฐ์ ์ธ ๊ฐ์†Œ๋ฅผ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ตญ๊ฐ€์•”์ •๋ณด์„ผํ„ฐ์˜ ์•” ์˜ˆ๋ฐฉ ๋ฐ ์‹ค์ฒœ์— ๋Œ€ํ•œ ๊ฒ€์ฆ๋œ ๋ฌธ์„œ๋“ค์—์„œ ์ถ”์ถœํ•œ 28,844๊ฐœ ๋ช…์ œ๋ฅผ ์œ„ํ‚คํ”ผ๋””์•„ ์•” ๊ด€๋ จ ๋ฌธ์„œ์—์„œ ์ถ”์ถœํ•œ 7,844๊ฐœ ๋ช…์ œ์— ์ฃผ์„ํ•˜๋Š” ์‚ฌ๋ก€๋ฅผ ํ†ตํ•˜์—ฌ ๊ธฐ์กด์˜ ์‹œ์Šคํ…œ๊ณผ ์žฌ๊ตฌ์ถ•ํ•œ ์‹œ์Šคํ…œ์„ ๋ณ‘๋ ฌ ๋น„๊ตํ•˜์˜€๋‹ค. ๋™์ผํ•œ ํ™˜๊ฒฝ์—์„œ ๊ธฐ์กด ์‹œ์Šคํ…œ์˜ ํ•™์Šต ์‹œ๊ฐ„์ด 553.8์ผ๋กœ ์ถ” ์ •๋œ ๊ฒƒ์— ๋น„ํ•ด ์žฌ๊ตฌ์ถ•ํ•œ ์‹œ์Šคํ…œ์€ 93.1๋ถ„ ๋‚ด๋กœ ํ•™์Šต์ด ์™„๋ฃŒ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์žฅ์ ์€ ์ธ๊ณต ์‹ ๊ฒฝ ์ •๋ฆฌ ์ฆ ๋ช…๊ณ„๊ฐ€ ๋ชจ๋“ˆํ™” ๊ฐ€๋Šฅํ•œ ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์ด๊ธฐ์— ๋‹ค๋ฅธ ์„ ํ˜• ๋…ผ๋ฆฌ ๋ฐ ์ž์—ฐ์–ธ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋“ˆ๋“ค๊ณผ ๋ณ‘๋ ฌ์ ์œผ๋กœ ๊ฒฐํ•ฉ ๋  ์ˆ˜ ์žˆ์Œ์—๋„ ํ˜„์‹ค ์‚ฌ๋ก€์— ์ด๋ฅผ ์ ์šฉ ๋ถˆ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ–ˆ๋˜ ํ•™์Šต ์‹œ๊ฐ„์— ๋Œ€ํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด์†Œํ–ˆ๋‹ค๋Š” ์ ์ด๋‹ค.

Prosodic and Linguistic Analysis of Semantic Fluency Data: A Window into Speech Production and Cognition

Maria Wolters, Najoung Kim, Jung-Ho Kim, Sarah E. MacPherson, and Jong C. Park
Interspeech 2016, pp. 2085-2089, San Francisco, California, September 8-12, 2016.
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Semantic fluency is a commonly used task in psychology that provides data about executive function and semantic memory. Performance on the task is affected by conditions ranging from depression to dementia. The task involves participants naming as many members of a given category (e.g. animals) as possible in sixty seconds. Most of the analyses reported in the literature only rely on word counts and transcribed data, and do not take into account the evidence of utterance planning present in the speech signal. Using data from Korean, we show how prosodic analyses can be combined with computational linguistic analyses of the words produced to provide further insights into the processes involved in producing fluency data. We compare our analyses to an established analysis method for semantic fluency data, manual determination of lexically coherent clusters of words.

Classification of Relations between Biological Entities using Word Vectors

Jimin Park, Jin-Woo Chung, and Jong C. Park
Proceedings of Korea Computer Congress (KCC), pp. 771-773, Jeju, Korea, June 29 - July 1, 2016. (poster presentation)
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์ƒ๋ฌผํ•™์  ์ฒด๊ณ„ ์•ˆ์—์„œ ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋…ผ๋ฌธ ํ…์ŠคํŠธ๋ฅผ ํ†ตํ•ด ์‹๋ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ, ์ผ๋ฐ˜์ ์ธ ๋‹จ์–ด ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„ํฌ ์˜๋ฏธ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ๋Š” ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ๊ฐ ์žˆ์—ˆ์œผ๋‚˜, ๋‘ ๋ฐฉ๋ฒ•์„ ๊ฒฐํ•ฉํ•œ ์‹œ๋„๋Š” ๊ฑฐ์˜ ๋ณด๊ณ ๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ„ํฌ ๋ชจ๋ธ์ด ์ƒ๋ฌผํ•™์ ์ธ ์ฒด๊ณ„ ์•ˆ์—์„œ ๋‘ ๊ตฌ์„ฑ์š”์†Œ๊ฐ€ ๋งบ๊ณ  ์žˆ๋Š” ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์–ด๋–ค ๊ธฐ์—ฌ๋ฅผ ํ•˜๋Š”์ง€ ์•Œ์•„๋ณด์•˜๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ๋ถ„ํฌ ๋ชจ๋ธ์ด ์ƒ๋ฌผํ•™์  ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„ ์‹๋ณ„์— ์œ ์šฉํ•œ ์ž์งˆ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ํ™•์ธํ•˜์˜€๋‹ค.

Addressing Low-Resource Problems in Statistical Machine Translation of Sign Language

Hancheol Park, Jung-Ho Kim, and Jong C. Park
Proceedings of Korea Computer Congress (KCC), pp. 714-716, Jeju, Korea, June 29 - July 1, 2016.
(selected as best paper)
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์ตœ๊ทผ ํ†ต๊ณ„์  ๊ธฐ๊ณ„ ๋ฒˆ์—ญ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์ˆ˜ํ™” ๋ฒˆ์—ญ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•ด์ง์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ณ‘๋ ฌ ๋ง๋ญ‰์น˜ ์ž์›์˜ ํฌ์†Œ์„ฑ ๋ฌธ์ œ๋Š” ์•„์ง ํ•ด๊ฒฐ๋˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ†ต๊ณ„์  ๊ธฐ๊ณ„ ๋ฒˆ์—ญ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ตฌ์–ด๋กœ ํ‘œํ˜„ ๋  ์ˆ˜ ์žˆ๋Š” ์–ธ์–ด๋ฅผ ์ˆ˜์ง€ ํ‘œํ˜„์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ์ˆ˜ํ™”๋กœ ๋ฒˆ์—ญ ํ•  ๋•Œ, ์ž์› ํฌ์†Œ์„ฑ์— ๊ธฐ์ธํ•˜๋Š” ๋ฌธ์ œ์ ๋“ค์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์„ธ ๊ฐ€์ง€ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ž์› ํฌ์†Œ์„ฑ ๋ฌธ์ œ๋ฅผ ์•ˆ๊ณ  ์žˆ๋Š” ์ˆ˜ํ™” ๋ฒˆ์—ญ์—์„œ ์‹ค์ œ๋กœ ๋ฒˆ์—ญ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋“ค์ด ๋ฌด์—‡์ธ์ง€๋ฅผ ์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์€ ๊ตฌ์–ด ๋ฌธ์žฅ์˜ ํŒจ๋Ÿฌํ”„๋ ˆ์ด์ง•์„ ํ†ตํ•œ ๋ง๋ญ‰์น˜ ํ™•์žฅ ๋ฐฉ๋ฒ•, ๊ตฌ์–ด ๋‹จ์–ด์˜ ํ‘œ์ œ์–ดํ™”๋ฅผ ํ†ตํ•œ ๊ฐœ๋ณ„ ์–ดํœ˜ ๋นˆ๋„๋ฅผ ๋†’์ด๋Š” ๋ฐฉ๋ฒ•, ๊ทธ๋ฆฌ๊ณ  ์ˆ˜์ง€ ์ •๋ณด๋กœ ํ‘œํ˜„๋˜์ง€ ์•Š๋Š” ๊ตฌ์–ด ํ’ˆ์‚ฌ์— ํ•ด๋‹นํ•˜๋Š” ๋‹จ์–ด๋ฅผ ์ œ๊ฑฐํ•จ์œผ๋กœ์จ ๊ตฌ์–ด์™€ ์ˆ˜ํ™” ๊ฐ„ ๋ฌธ์žฅ ์„ฑ๋ถ„์„ ์ผ์น˜์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ์˜์–ด์™€ ๋ฏธ๊ตญ ์ˆ˜ํ™” ๋ณ‘๋ ฌ ๋ง๋ญ‰์น˜๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ์„ธ ๊ฐ€์ง€ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ• ์ค‘ ํŒจ๋Ÿฌํ”„๋ ˆ์ด์ฆˆ ์ƒ์„ฑ ๋ฐ ํ‘œ์ œ์–ดํ™”์˜ ์ ์šฉ ์‹œ์—๋งŒ ๋ฒˆ์—ญ ํ’ˆ์งˆ์ด ํ–ฅ์ƒ๋œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ, ๋‘ ๋ฐฉ๋ฒ•์ด ๊ฐ™์ด ์ ์šฉ๋  ๋•Œ ๊ฐ€์žฅ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.

A Morphological Approach to the Longitudinal Detection of Dementia

Najoung Kim and Jong C. Park
HCI Conference Korea, High1 Resort, Gangwon, January 27-29, 2016.
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The impact of cognitive impairment on linguistic abilities has been a topic of continuous interest in dementia studies. However, there is a lack of systematic agreement on the longitudinal association between dementia progression and the patients' morphological capacity, and the role of morphological phenomena other than inflection has been relatively underreported. We present a longitudinal study of writings by Iris Murdoch (diagnosed of Alzheimer's Disease after her death) and Arthur Conan Doyle (no known record of dementia diagnosis), using two novel measures to account for the usage of complex morphology and lexical innovation. The results imply an association between lexical innovation and cognitive decline caused by dementia, as observed in Murdoch's works beginning from her mid-fifties, in contrast to a milder tendency in Doyle's works. Our findings contribute to a potential for facilitating early diagnosis of dementia through automated language processing approaches.

Biomedical Event Extraction and Management in Big-scale Biomedical Literature

Rize Jin, Jinseon You, and Jong C. Park
42nd KIISE Winter Conference, Phoenix Park, December 17-19, 2015. (poster presentation)
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๋Œ€์šฉ๋Ÿ‰ ์ƒ๋ฌผํ•™ ๋ฌธํ—Œ ์ •๋ณด๊ฐ€ ์ถ•์ ๋จ์— ๋”ฐ๋ผ ์ƒ๋ฌผํ•™ ์—ฐ๊ตฌ์ž๋“ค์˜ ์—ฐ๊ตฌ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๋•๊ธฐ ์œ„ํ•œ ๋ฌธํ—Œ ์ •๋ณด ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์ด๋‚˜ ๊ฒ€์ƒ‰ ์—”์ง„๊ณผ ๊ฐ™์€ ๋„๊ตฌ๋“ค์ด ๋“ฑ์žฅํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋„๊ตฌ๋“ค์€ ์ƒ๋ฌผํ•™ ์—ฐ๊ตฌ์— ๋งŽ์€ ๋„์›€์„ ์ฃผ๊ณ  ์žˆ์œผ๋‚˜ ๋ณต์žกํ•œ ์—ฐ์‚ฐ ์ฒ˜๋ฆฌ์— ์žˆ์–ด์„œ๋Š” ์•„์ง ๋ถ€์กฑํ•œ ์ ์ด ๋งŽ์€ ์‹ค์ •์ด๋‹ค. ํŠนํžˆ ๊ฒ€์ƒ‰์—”์ง„์˜ ๊ฒฝ์šฐ ๋‹จ์–ด ์ˆ˜์ค€์˜ ์งˆ์˜์–ด๋Š” ์‰ฝ๊ฒŒ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ์œผ๋‚˜ ๋‹จ์–ด ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ณต์žกํ•œ ์งˆ์˜์–ด์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง ์ฒ˜๋ฆฌ ์ˆ˜์ค€์ด ๋ฏธํกํ•˜๋‹ค. ์ด์— ์ƒ๋ฌผํ•™ ์–ธ์–ด ์ฒ˜๋ฆฌ ๋ถ„์•ผ์—์„œ๋Š” ๋ณต์žกํ•œ ์งˆ์˜์–ด๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ์œ ์ „์ž ์‹๋ณ„, ์ƒ๋ฌผํ•™ ์ด๋ฒคํŠธ ์‹๋ณ„๊ณผ ๊ฐ™์€ ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜์—ˆ์œผ๋ฉฐ ์ƒ๋‹นํ•œ ์ˆ˜์ค€์˜ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ์‹œ์Šคํ…œ๋“ค์€ ์ „๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๋ณต์žกํ•œ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•จ์— ๋”ฐ๋ผ ๋Œ€์šฉ๋Ÿ‰ ์ฒ˜๋ฆฌ์—๋Š” ์ ํ•ฉํ•˜์ง€ ์•Š๊ฒŒ ์„ค๊ณ„๋˜์—ˆ๊ณ  ์ด๋Š” ์ƒ๋ฌผํ•™ ์–ธ์–ด ์ฒ˜๋ฆฌ ๋ถ„์•ผ์— ๋Œ€์šฉ๋Ÿ‰ ์ฒ˜๋ฆฌ๊ฐ€ ์ ์  ๋” ํ•„์š”ํ•ด์ง€๋ฉด์„œ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ๋กœ ๋Œ€๋‘ ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์ธ ํ•˜๋‘ก์„ ์ด์šฉํ•ด ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ์‹œ์Šคํ…œ ์ค‘ ํ•˜๋‚˜์ธ ์ด๋ฒคํŠธ ์‹๋ณ„ ์‹œ์Šคํ…œ์ด ๋Œ€์šฉ๋Ÿ‰ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋„๋ก ์‹œ์Šคํ…œ์„ ๊ณ ๋„ํ™” ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ๋‹ค.

A New Measure of Clustering and Switching Based on Bigrams

Maria Wolters, Sarah MacPherson, Jinseon You, Rize Jin, Seung-Cheol Baek, and Jong C. Park
Psychonomic Society's 56th Annual Meeting, Chicago, USA, November 19-22, 2015. (poster presentation)
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The category fluency task (CFT) provides important information about executive abilities such as initiation set-shifting and inhibition. CFT sequences are generated by retrieving groups of related words (โ€œclustersโ€œ) from semantic memory. Manual annotation schemes have been developed for inferring these clusters from transcribed CFT sequences (Troyer 2008), but these are time-consuming and require training. We propose an automatic analysis technique that is based on a simple statistical model of CFT sequences. This model can be easily adapted to different languages and domains, given sufficient training data. CFT sequences (domain โ€œanimalsโ€œ) were generated by 104 younger adults aged 18-34 years and 100 older adults aged 50-84 years who were native speakers of UK English. The sequences were categorised both manually and using our automated method with key measures such as the number of switches significantly correlating (rho=0.4, 95% CI [0.28-0.51]). Both methods also resulted in the significant age differences that are consistently reported in the cognitive aging literature.

Corpus Annotation with a Linguistic Analysis of the Associations between Event Mentions and Spatial Expressions

Jin-Woo Chung, Jinseon You, and Jong C. Park
Proceedings of the 29th Pacific Asia Conference on Language, Information, and Computation (PACLIC 29), pp. 539-547, Shanghai, China, October 30-November 1, 2015.
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Recognizing spatial information associated with events expressed in natural language text is essential for the proper interpretation of such events. However, the associations between events and spatial information found throughout the text have been much less studied than other types of spatial association as looked into in SpatialML and ISO-Space. In this paper, we present an annotation framework for the linguistic analysis of the associations between event mentions and spatial expressions in broadcast news articles. Based on the corpus annotation and analysis, we discuss which information should be included in the guidelines and what makes it difficult to achieve a high inter-annotator agreement. We also discuss possible improvements on the current corpus and annotation framework for insights into developing an automated system.

A System for Constructing a Korean-to-KSL Parallel Corpus

Jung-Ho Kim, Umang Sehgal, and Jong C. Park
17th Annual Conference on Korean Sign Language, Kongju University, Gongju, Korea, August 15, 2015. (poster presentation)
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ํ•œ๊ตญ์–ด-ํ•œ๊ตญ์ˆ˜์–ด ๋ณ‘๋ ฌ ๋ง๋ญ‰์น˜๋Š” ๊ด€๋ จ ์‚ฌ์ „์ด๋‚˜ ์ž๋™ ๋ฒˆ์—ญ ์‹œ์Šคํ…œ์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์–ด ๊ธด์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ๋ฐ˜ ๋ณ‘๋ ฌ ๋ง๋ญ‰์น˜ ๊ตฌ์ถ•๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ์ˆ˜์–ด์˜ ๊ณต๊ฐ„ ์–ธ์–ด์ ์ธ ํŠน์„ฑ ๋•Œ๋ฌธ์— ๊ตฌ์ถ•์ด ์šฉ์ดํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํšจ์œจ์ ์œผ๋กœ ํ•œ๊ตญ์–ด-ํ•œ๊ตญ์ˆ˜์–ด ๋ณ‘๋ ฌ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค.

CoMAGD: Annotation of Gene-Depression Relations

Rize Jin, Jinseon You, Jin-Woo Chung, Hee-Jin Lee, Maria Wolters, and Jong C. Park
Proceedings of the 2015 ACL Workshop on Biomedical Natural Language Processing (BioNLP 2015), pp. 104-113, Beijing, China, July 30, 2015.
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Clinical depression is a mental disorder involving genetics and environmental factors. Although much work studied its genetic causes and numerous candidate genes have consequently been looked into and reported in the biomedical literature, no gene expression changes or mutations regarding depression have yet been adequately collected and analyzed for its full pathophysiology. In this paper, we present a depression-specific annotated corpus for text mining systems that target at providing a concise review of depression-gene relations, as well as capturing complex biological events such as gene expression changes. We describe the annotation scheme and the conducted annotation procedure in detail. We discuss issues regarding proper recognition of depression terms and entity interactions for future approaches to the task. The corpus is available at http://www.biopathway.org/CoMAGD.

Identification of Depression-Gene Associations from Biomedical Literature

Jinseon You, Rize Jin, Hee-Jin Lee, and Jong C. Park
Korea Computer Congress (KCC), Jeju, Korea, June 24-26, 2015.
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์šฐ์šธ์ฆ์€ ํ˜„๋Œ€์ธ๋“ค์ด ๊ฒช๋Š” ๋Œ€ํ‘œ์ ์ธ ์ •์‹  ์งˆํ™˜์œผ๋กœ ๊ด€๋ จ ํ˜ธ๋ฅด๋ชฌ ๋ถ„๋น„๋Ÿ‰์— ๋”ฐ๋ผ ์ฆ์„ธ๊ฐ€ ๋‹ฌ๋ผ์ง€๊ณ  ์ด๋Š” ๋˜ํ•œ ๊ด€๋ จ ์œ ์ „์ž ํ‘œํ˜„ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง„๋‹ค. ์šฐ์šธ์ฆ ๊ด€๋ จ ์œ ์ „์ž๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ์ด๋“ค๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋ฐํ˜€๋‚ธ๋‹ค๋ฉด ํ•ญ์šฐ์šธ์ œ ๊ฐœ๋ฐœ์— ๋งŽ์€ ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค. ํ˜„์žฌ ์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ํ™œ๋ฐœํžˆ ์ง„ํ–‰ ์ค‘์— ์žˆ์œผ๋‚˜ ๊ด€๋ จ๋œ ๋ชจ๋“  ์œ ์ „์ž๋ฅผ ํ•œ ๋ฒˆ์— ํŒŒ์•…ํ•˜๊ธฐ๋Š” ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์•”๊ณผ ์œ ์ „์ž๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์ฐพ๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ๋„์ž…ํ•˜์—ฌ ์šฐ์šธ์ฆ๊ณผ ์œ ์ „์ž๊ฐ„ ๊ด€๊ณ„๋ฅผ ์ž๋™์œผ๋กœ ํŒŒ์•…ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•œ๋‹ค. ์ด๋Š” ํ–ฅํ›„ ์šฐ์šธ์ฆ๊ณผ ์œ ์ „์ž ๊ฐ„์˜ ์‹ฌํ™”๋œ ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๋Š”๋ฐ ํ•„์š”ํ•œ ์ฝ”ํผ์Šค ์ œ์ž‘์— ํฐ ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.

Construction of a Korean-to-KSL Parallel Corpus by Effective Motion Capture of Hand Shapes

Jung-Ho Kim and Jong C. Park
41st KIISE Winter Conference, Phoenix Park, December 18-20, 2014. (poster presentation)
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๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์–ด์™€ ํ•œ๊ตญ์ˆ˜์–ด ๊ฐ„์˜ ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค๋ฅผ ์ œ์ž‘ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜ํ˜•(Hand Shape)์˜ ํšจ์œจ์  ์ˆ˜์ง‘ ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๋ฉฐ, ์†๋™์ž‘ ๋ฒ”์œ„์— ํ•œํ•˜์—ฌ ์ˆ˜์–ด ๋™์ž‘์„ ์ธ์‹ ๋ฐ ์ˆ˜์ง‘ํ•˜๊ธฐ ์œ„ํ•ด ๋ฆฝ๋ชจ์…˜(Leap Motion)์„ ์ด์šฉํ•œ๋‹ค. ์ œ์‹œํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์ œ์ž‘๋œ ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด 46๊ฐœ์˜ ์ˆ˜์–ด ๋™์ž‘์„ ์ˆ˜์ง‘ํ•˜์˜€๊ณ , ๋ฏธ๋ฆฌ ์ˆ˜์ง‘๋˜์ง€ ์•Š์€ 54๊ฐœ์˜ ์ˆ˜์–ด ๋™์ž‘์„ ์ถ”๊ฐ€ ์„ ๋ณ„ํ•˜์—ฌ ์ด 100๊ฐœ์˜ ์ˆ˜์–ด์— ๋Œ€ํ•ด ํ‰๊ท  42.15%์˜ ์ •ํ™•๋„์™€ 58.72%์˜ ์žฌํ˜„์œจ์„ ๊ฐ€์ง€๋Š” ์ธ์‹ ์ˆ˜์ค€์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฐฉ์•ˆ์€ ๋งค์šฐ ๋ณดํŽธ์ ์ด์–ด์„œ ๋Œ€๊ทœ๋ชจ ๋ฐ ๋™์‹œ์ ์œผ๋กœ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์ธ๋‹ค.

An Effective Construction of a Korean-to-KSL Parallel Corpus

Jung-Ho Kim and Jong C. Park
Proceedings of the 26th Annual Conference on Human and Cognitive Language Technology (HCLT), pp. 13-17, ChunCheon, Korea, October 10-11, 2014.
(selected as best paper)
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๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์–ด์™€ ํ•œ๊ตญ์ˆ˜ํ™” ๊ฐ„์˜ ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค ์ œ์ž‘๊ณผ ํ•จ๊ป˜ ์ด์— ๋”ฐ๋ฅธ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ œ์ž‘ํ•˜๊ธฐ ์œ„ํ•ด ํ‚ค๋„ฅํŠธ์™€ ๋ฆฝ๋ชจ์…˜์„ ์ด์šฉํ•˜์˜€๊ณ , ์ด์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•˜๊ณ  ์žˆ๋Š” ์žฅ๊ฐ‘์„ ํ†ตํ•œ ๋™์ž‘ ์ธ์‹ ๋ฐ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•๊ณผ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•˜๊ณ  ์žˆ๋Š” ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ, ๋น„๊ต ๊ฒฐ๊ณผ ์žฅ๊ฐ‘์„ ํ†ตํ•ด ์ˆ˜์ง‘ํ•œ ๊ฒฐ๊ณผ์™€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ์ฐจ์ด๊ฐ€ ๋‚˜์ง€ ์•Š์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ๋ณธ ์—ฐ๊ตฌ์˜ ๋™์ž‘ ์ˆ˜์ง‘ ๋ฐฉ์‹์ด ์ƒ๋Œ€์ ์œผ๋กœ ๊ณ ๋น„์šฉ์ธ ์žฅ๊ฐ‘ ์ˆ˜์ง‘ ๋ฐฉ์‹๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ฒฝ์Ÿ๋ ฅ์ด ์žˆ์Œ์„ ์‹œ์‚ฌํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ๋ณดํŽธ์ ์ธ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜๋Š” ํŠน์ง•๊นŒ์ง€ ๊ฐ€์ง€๊ณ  ์žˆ์–ด์„œ ๋™์‹œ์ ์œผ๋กœ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ์–ด ๊ทœ๋ชจ๊ฐ€ ์žˆ๋Š” ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค ๊ตฌ์ถ•์„ ๋”์šฑ ํšจ์œจ์ ์œผ๋กœ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.

On Mention-Level Gene Normalization

Joon-Yeob Kim, Seung-Cheol Baek, Hee-Jin Lee, and Jong C. Park
5th International Symposium on Languages in Biology and Medicine (LBM 2013), Tokyo, Japan, 12th and 13th December, 2013.
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Document-level gene normalization (DGN), which produces a list of gene identifiers relevant to an input document, helps database curators to search for articles of interest by indexing articles with gene identifiers. Recent advances in automatic extraction of information from the biology literature call for mention-level gene normalization (MGN) systems. However, there have been no annotated corpora for MGN, probably because of a somewhat unfounded assumption (convertibility assumption) that it might be straightforward to map gene mentions into gene identifiers given a list of gene identifiers for the document. In the present work, we constructed gold standard annotations for the MGN task and assessed the validity of the convertibility assumption with GeneTUKit (Huang et al., 2011), a state-of-the-art DGN system.

Parsing Dependency Paths to Identify Event-Argument Relation

Seung-Cheol Baek and Jong C. Park
Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP), Nagoya, Japan, October 15-17, 2013, pp. 699-705.
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Mentions of event-argument relations, in particular dependency paths between event-referring words and argument-referring words, can be decomposed into meaningful components arranged in a regular way, such as those indicating the type of relations and the others allowing relations with distant arguments (e.g., coordinate conjunction). We argue that the knowledge about arrangements of such components may provide an opportunity for making event extraction systems more robust to training sets, since unseen patterns would be derived by combining seen components. However, current state-of-the-art machine learning based approaches to event extraction tasks take the notion of components at a shallow level by using n-grams of paths. In this paper, we propose two methods called pseudo-count and Bayesian methods to semi-automatically learn PCFGs by analyzing paths into components from the BioNLP shared task training corpus. Each lexical item in the learned PCFGs appears in 2.6 distinct paths on average between event-referring words and argument-referring words, suggesting that they contain recurring components. We also propose a grounded way of encoding multiple parse trees for a single dependency path into feature vectors in linear classification models. We show that our approach can improve the performance of identifying event-argument relations in a statistically significant manner.

Speaker-TTS Voice Mapping towards Natural and Characteristic Robot Storytelling

Hye-Jin Min, Sang-Chae Kim, Joon-Yeob Kim, Jin-Woo Chung, and Jong C. Park
Proceedings of the 22nd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2013), pp. 793-800, Gyeongju, Korea, August 26-29, 2013.
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Robot storytelling has the potential for its practical use in various domains such as entertainment, education, and rehabilitation. However, relying on human-recorded voices for natural storytelling is costly, and automation with text-to-speech systems is not readily applicable due to the difficulty of reflecting the full nature of stories in TTS systems. In this paper, we address the problem of automating robot storytelling with a particular focus on two issues: speaker identification and speaker-TTS voice mapping. We first conduct text analysis with rich linguistic clues to identify speakers from a given textual story. We then consider the task of speaker-TTS voice mapping as the graph coloring problem and propose effective algorithms for assigning voices to speakers given a limited number of TTS voices. Finally, we perform a user experiment on validating the usefulness of our method. The results demonstrate that our system significantly outperforms baseline systems and is also more acceptable to users.

Enhancing Readability of Web Documents by Text Augmentation for Deaf People

Jin-Woo Chung, Hye-Jin Min, JoonYeob Kim, and Jong C. Park
International Conference on Web Intelligence, Semantics, and Mining (WIMS), Madrid, Spain, June 12-14, 2013.
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Deaf people have particular difficulty in understanding text-based web documents because their mother language, or sign language, is essentially visually oriented. To enhance the readability of text-based web documents for deaf people, we propose a news display system that converts complex sentences in news articles into simple sentences and presents the relations among them with a graphical representation. In particular, we focus on the tasks of 1) identifying subordinate and embedded clauses in complex sentences, 2) relocating them for better readability and 3) displaying the relations among the clauses with the graphical representation. The results of our evaluation show that the proposed system does simplify complex sentences in news articles effectively while maintaining their intended meaning, suggesting that our system can be used in practice to help deaf people to access textual information.

Blog Corpus-based Clustering Scheme for Category Fluency Test (CFT) Data Clustering

Yong-Jae Lee, Maria Wolters, Hee-Jin Lee, and Jong C. Park
HCI Conference Korea, High1 Resort, Gangwon, Jan. 30-Feb. 1, 2013.
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Category Fluency Test (CFT) is one of the most popular methods to screen dementia and is used in particular to evaluate the organization of the semantic memory and verbal fluency of a patient with dementia. The CFT performance is assessed according to the number of items each patient produces during the test. Recently, however, researchers have also proposed to evaluate the performance by considering the pattern of clusters and switches of the CFT data, with efforts to figure out the clusters and switches on the CFT data computationally. In this work, we propose a novel blog corpus-based clustering scheme to analyze the clusters and switches of the CFT data in a computational manner. In addition, we will argue for the need of the blog corpus-based clustering scheme by comparing it with the previous work on automatic CFT data clustering.

Analyzing and Mapping Expressions of Tense for Korean-Korean Sign Language Translation

JoonYeob Kim, Jin-Woo Chung, and Jong C. Park
Proceedings of the KIISE Fall Conference, Vol. 39 No. 2-B, pp. 121-123, Chungnam National University, November 23-24, 2012.
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์ˆ˜ํ™”๋Š” ๋†์ธ ์‚ฌํšŒ์—์„œ ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์‹œ๊ฐ์–ธ์–ด๋กœ์„œ ์Œ์„ฑ์–ธ์–ด์ธ ํ•œ๊ตญ์–ด์™€ ํ‘œํ˜„ ์–‘์‹์—์„œ ๋งŽ์€ ์ฐจ์ด๋ฅผ ๋ณด์ธ๋‹ค. ํŠนํžˆ ํ•œ๊ตญ์–ด์—์„œ๋Š” ํŠน์ • ๋ฌธ๋ฒ•ํ˜•ํƒœ์†Œ๋ฅผ ์„œ์ˆ ์–ด์™€ ๊ฒฐํ•ฉ์‹œํ‚ด์œผ๋กœ์จ ์‹œ์ œ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ด๋Š” ๋ฐ˜๋ฉด์—, ์ˆ˜ํ™”์˜ ๊ฒฝ์šฐ ์„œ์ˆ ์–ด์™€ ๊ฒฐํ•ฉํ•˜๋Š” ํ˜•ํƒœ์†Œ๋‚˜ ์‹œ์ œ๋ฅผ ์œ„ํ•œ ๋ณ„๋„์˜ ๊ธฐ๋Šฅ์–ด๊ฐ€ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์„œ์ˆ ์–ด์˜ ์‹œ์ œ ํ‘œํ˜„์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์ด ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด-์ˆ˜ํ™” ๋ณ‘๋ ฌ ๋ฐ์ดํ„ฐ์˜ ๊ฐ ๋ฌธ์žฅ์— ๋‚˜ํƒ€๋‚˜๋Š” ์‹œ์ œ ํ‘œํ˜„์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์ฃผ์–ด์ง„ ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์„ ์ ์ ˆํ•œ ์ˆ˜ํ™” ๋ฌธ์žฅ์œผ๋กœ ๋ณ€ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด ํ•„์š”ํ•œ ์‹œ์ œ ํ‘œํ˜„ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ๋…ผ์˜ํ•œ๋‹ค.

Product Name Classification for Product Instance Distinction

Hye-Jin Min and Jong C. Park
The 26th Pacific Asia Conference on Language, Information, and Computation (PACLIC 26), Bali, Indonesia, November 7-10, 2012.
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Product names with a temporal cue in a product review often refer to several product instances purchased at different times. Previous approaches to product entity recognition and temporal information analysis do not take into account such temporal cues and thus fail to distinguish different product instances. We propose to formulate the resolution of such product names as a classification problem by utilizing time expressions, event features and other temporal cues for a classifier in two stages, detecting the existence of such temporal cues and identifying the purchase time. The empirical results show that term-based features and existing event-based features together enhance the performance of product instance distinction.

Automatic Speaker Identification in Fairytales towards Robot Storytelling

Hye-Jin Min, Sang-Chae Kim, and Jong C. Park
Proceedings of the 24th Annual Conference on Human and Cognitive Language Technology (HCLT), pp. 77-84, Busan, Korea, October 12-13, 2012.
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๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์˜ ์ž๋™ ๋™ํ™”๊ตฌ์—ฐ์„ ๋ชฉํ‘œ๋กœ ๋ฐœํ™”๋ฌธ์žฅ ์ƒ์˜ ๊ฐ์ • ํŒŒ์•… ๋ฐ ๋“ฑ์žฅ์ธ๋ฌผ ๋ณ„ ๋‹ค์–‘ํ•œ TTS ๋ณด์ด์Šค ์„ ํƒ์— ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ๋ฐœํ™”๋ฌธ์žฅ์˜ ํ™”์ž ํŒŒ์•…๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด ๊ทœ์น™๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ์—์„œ ๋งŽ์ด ํ™œ์šฉ๋˜์–ด์˜จ ์ž์งˆ์ธ ํ›„๋ณด์˜ ์œ„์น˜, ํ™”์ž ํ›„๋ณด์˜ ์ฃผ๊ฒฉ/๋ชฉ์ ๊ฒฉ ์—ฌ๋ถ€, ๋ฐœํ™”๋™์‚ฌ ์กด์žฌ ์—ฌ๋ถ€๋ฅผ ๋น„๋กฏํ•˜์—ฌ ๋™ํ™”์— ์ž์ฃผ ๋‚˜ํƒ€๋‚˜๋Š” ๋“ฑ์žฅ์ธ๋ฌผ์˜ ์˜๋ฏธ์  ๋ถ„๋ฅ˜ ๋ฐ ๋“ฑ์žฅ์ธ๋ฌผ์˜ ๋“ฑ์žฅ/ํ‡ด์žฅ๊ณผ ๊ด€๋ จ๋œ ๋™์‚ฌ๋“ค์„ ์ถ”๊ฐ€ ์ž์งˆ๋กœ ํ™œ์šฉํ•œ๋‹ค. ์‚ฌ๋žŒ ๋ฐ ๋™์‹๋ฌผ, ๋ฌด์ƒ๋ฌผ์ด ๋ชจ๋‘ ํ™”์ž๊ฐ€ ๋  ์ˆ˜ ์žˆ๋Š” ๋™ํ™” ์ฝ”ํผ์Šค์—์„œ ์ œ์•ˆํ•œ ์ž์งˆ๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ์˜์‚ฌ๊ฒฐ์ •ํŠธ๋ฆฌ๋กœ ํ•™์Šต ๋ฐ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ๊ทœ์น™๊ธฐ๋ฐ˜์˜ ๋ฒ ์ด์Šค๋ผ์ธ ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ์ตœ๋Œ€ 49%์˜ ์ •ํ™•๋„๊ฐ€ ํ–ฅ์ƒ๋˜์—ˆ๊ณ , ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ๋ฐ์ดํ„ฐ์˜ ๋ณ€ํ™”์—๋„ ๊ฐ•์ธํ•œ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.

Use of Clue Word Annotations as the Silver-standard in Training Models for Biological Event Extraction

Seung-Cheol Baek and Jong C. Park
Proceedings of the 5th International Symposium on Semantic Mining in Biomedicine (SMBM 2012), pp. 34-41, University of Zurich, Switzerland, September 3-4, 2012.
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Current state-of-the-art approaches to biological event extraction train models by reconstructing relevant graphs from training sentences, where labeled nodes correspond to tokens that indicate the presence of events and the relations between nodes correspond to the relations between these events and their participants. Since multi-word expressions may also indicate events, these approaches use heuristic rules to define target graphs to reconstruct by mapping various clue words into single tokens. Since training instances define actual problems to solve, the method of deriving graphs must affect the system performance, but there has not been any related study on this aspect, to the best of our knowledge. In this study, we propose an incorporation of an EM algorithm into supervised learning to look for training graphs that are more favorable for model construction. We evaluate our algorithm on the development dataset in the 2009 BioNLP shared task and show that this algorithm makes a statistically meaningful improvement on the performance of trained models over a supervised learning algorithm on a fixed set of training graphs. The models and graphs are available at http://biopathway.org/EventExtraction/.

Towards Automatic Evaluation of Category Fluency Test Performance: Distinguishing Groups using Word Clustering

Yong-Jae Lee, Maria Wolters, Hee-Jin Lee, and Jong C. Park
Korea Computer Congress (KCC), Jeju, Korea, June 27-29, 2012.
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The Category Fluency Test (CFT) is a widely used verbal fluency test. The standard measure of scoring the test is the number of distinct words that a subject generates during the test. Recently, other measures have also been proposed to evaluate performance, such as clustering and switching. In this study, we examine clusters and switches can be assessed using word similarity measures. Based on these measures, we can distinguish between subject groups.

Age and Gender Prediction from Korean Tweets with Stylometric Analysis

Sang-Chae Kim and Jong C. Park
Korea Computer Congress (KCC), Jeju, Korea, June 27-29, 2012.
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์‚ฌ๋žŒ๋“ค์€ ์ฃผ๋ณ€์˜ ์˜ํ–ฅ์„ ๋ฐ›์•„ ๊ฐ€๋ฉด์„œ ๊ฐ์ž์˜ ๋…ํŠนํ•œ ๊ธ€์“ฐ๊ธฐ ์–‘์‹์„ ๋งŒ๋“ค์–ด๊ฐ„๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ™์€ ์—ฐ๋ น๋Œ€์™€ ์„ฑ๋ณ„์„ ๊ฐ€์ง€๋Š” ์‚ฌ๋žŒ๋“ค์€ ์œ ์‚ฌํ•œ ๊ธ€์“ฐ๊ธฐ ์–‘์‹์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๊ฐ€์ •์„ ๋ฐ”ํƒ•์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ์—ฐ๋ น๋Œ€์™€ ์„ฑ๋ณ„์˜ ์‚ฌ๋žŒ๋“ค์ด ์ž‘์„ฑํ•œ ํŠธ์œ—์˜ ๋ฌธ์ฒด๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ž„์˜์˜ ํŠธ์œ—์„ ์ž‘์„ฑํ•œ ์ €์ž์˜ ์—ฐ๋ น๋Œ€์™€ ์„ฑ๋ณ„์„ ์˜ˆ์ธกํ•˜๋Š” ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.
ํ•œ๊ตญ์–ด ์›น ์–ธ์–ด์—์„œ ์ž์ฃผ ๋ณด์ด๋Š” ํ‘œํ˜„๋“ค์„ ํ† ๋Œ€๋กœ ๊ตฌ์„ฑํ•œ ์ž์งˆ๋“ค๊ณผ, ๊ทธ์— ๋น„ํ•ด ๋ฐ์ดํ„ฐ์™€ ๊ด€๊ณ„๊ฐ€ ์ ์€ n-gram ๋‹จ์œ„์˜ ์ž์งˆ๋“ค์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜์—ฌ ์˜ˆ์ธก์„ ์ง„ํ–‰ํ•จ์œผ๋กœ์จ, ์ตœ๋Œ€ ๊ณต์‚ฐ ๊ธฐ์ค€์น˜๋ณด๋‹ค 25% ๊ฐ€๋Ÿ‰ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์ด๋Š” ์˜ˆ์ธก ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์™€ ํ•จ๊ป˜ ๊ฐ ์ž์งˆ ๊ตฌ์„ฑ์ด ์˜ˆ์ธก์— ์–ผ๋งˆ๋‚˜ ํšจ์œจ์ ์œผ๋กœ ๊ธฐ์—ฌํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค.

Analyzing the Patterns of Switching and Clustering on CFT Data Using Hidden Markov Model

Yong-Jae Lee, Hee-Jin Lee, Maria Wolters, and Jong C. Park
HCI Conference Korea, Alpensia resort, January 11-13, 2012.
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Early detection of dementia allows people to have more time to prepare themselves for the symptom. As one of the methods to screen dementia, Category Fluency Test (CFT) is used to evaluate the organization of semantic memory and to assess the verbal fluency performance of patients with dementia. Recently, various measures to evaluate their CFT performance have been studied and, in particular, clusters and switches of the CFT data are considered as important factors. In this work, we analyze the clusters and switches of the CFT data by using Hidden Markov Model (HMM) to verify the hypothesis that a comprehensive pattern analysis of their switches and clusters can reveal important characteristics of verbal fluency performance.

Age Prediction from Korean Tweets with Style-Based Feature Analysis

Sang-Chae Kim and Jong C. Park
HCI Conference Korea, Alpensia resort, January 11-13, 2012.
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Authorship attribution is a task of predicting the author from analyzing his/her writing. An increasing popularity of the Internet has made it easy for the authorship attribution researchers to access large corpora with annotated authorship. Such large corpora have enabled the researchers to predict the authorsโ€™ demographic characteristics such as age. In this paper, we analyze tweets in Korean with a small number of style-based features such as emoticons and propose a way of using these features to predict the age group. Our prediction resulted in a relatively high accuracy of 0.75

Analyzing Disagreements among ICD-9-CM Coders

Seung-Cheol Baek and Jong C. Park
4th International Symposium on Languages in Biology and Medicine (LBM 2011), Nanyang Technological University, Singapore, December 14-15, 2011.
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NLP researchers find it difficult to acquire and interpret clinical free text directly, most likely because of the unfamilarity with medical practices. This is why publicly available annotated corpora would be of much help, but there are still very few in the clinical domain due to patient confidentiality. In this regard, it is encouraging to see that Computational Medicine Centerโ€™s 2007 Challenge provides a publicly available corpus consisting of radiology reports with ICD-9-CM codes as independently assigned by three different coders. However, the corpus shows many disagreements among the coders, making it imperative to set the standard correctly for their proper interpretation. A proposal for such a standard as implicitly advanced by its developers is to take the majority annotation. In this paper, we propose an alternative method to address such disagreements. We believe our work not only makes a meaningful improvement on the utility of this corpus but also has good implications for similar tasks, such as ICD-10-CM coding.

Identifying Gene Expression Changes in Prostate Cancer Cells from the Literature

Hee-Jin Lee, Hyunju Lee, and Jong C. Park
4th International Symposium on Languages in Biology and Medicine (LBM 2011), Nanyang Technological University, Singapore, December 14-15, 2011.
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We propose to identify information about gene expression changes in diseased cells from the literature, utilizing event extraction techniques. Gene expression changes in a diseased cell or tissue happen when its expression level is either higher or lower than the level in normal states. Such information can be critically used in the next stage of understanding the molecular mechanisms of the disease, leading naturally to its pathway. In this work, we focus on prostate cancer (PC), one of the most troubling cancers.

Detecting and Blocking False Sentiment Propagation

Hye-Jin Min and Jong C. Park
Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP), pp. 354โ€“362, Chiang Mai, Thailand, November 8-13, 2011.
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Sentiment detection of a given expression involves interaction with its component constituents through rules such as polarity propagation, reversal or neutralization. Such compositionality-based sentiment detection usually performs better than a vote-based bag-of words approach. However, in some contexts, the polarity of the adjectival modifier may not always be correctly determined by such rules, especially when the adjectival modifier characterizes the noun so that its denotation becomes a particular concept or an object in customer reviews. In this paper, we examine adjectival modifiers in customer review sentences whose polarity should either be propagated (SHIFT) or not (UNSHIFT). We refine polarity propagation rules in the literature by considering both syntactic and semantic clues of the modified nouns and the verbs that take such nouns as arguments. The resulting rules are shown to work particularly well in detecting cases of โ€˜UNSHIFTโ€™ above, improving the performance of overall sentiment detection at the clause level, especially in โ€˜neutralโ€™ sentences. We also show that even such polarity that is not propagated is still necessary for identifying implicit sentiment of the adjacent clauses.

Text Parsing for Sign Language Generation with Combinatory Categorial Grammar

Jin-Woo Chung and Jong C. Park
2nd International Workshop on Sign Language Translation and Avatar Technology (SLTAT), 13th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS), University of Dundee, UK, October 23, 2011.
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In this paper, we propose a method to convert a written sentence in spoken language into a suitable representation in sign language within the framework of Combinatory Categorial Grammar (CCG). The representation reflects the multi-channel nature of sign language performance, including manual and non-manual linguistic signals of multiple channels and information about their coordination. We show that most information needed to address linguistic phenomena in sign language such as word order, spatial references, classifier construction, and verb inflection can be encoded in the CCG sign lexicon. During the CCG derivation process, a semantic representation for sign language expressions is created so that the resulting output can be directly interpreted as a sequence of signs, each containing manual and non-manual components and representing their coordination and spatial relationship. The derivation process with the constructed lexicon is presented with several examples for Korean Sign Language. We discuss implications of our proposal and future directions.

Revisiting Concatenative Video Synthesis with Relaxed Constraints

Sangyong Gil and Jong C. Park
2nd International Workshop on Sign Language Translation and Avatar Technology (SLTAT), 13th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS), University of Dundee, UK, October 23, 2011.
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In this paper, we propose a method to convert a written sentence in spoken language into a suitable representation in sign language within the framework of Combinatory Categorial Grammar (CCG). The representation reflects the multi-channel nature of sign language performance, including manual and non-manual linguistic signals of multiple channels and information about their coordination. We show that most information needed to address linguistic phenomena in sign language such as word order, spatial references, classifier construction, and verb inflection can be encoded in the CCG sign lexicon. During the CCG derivation process, a semantic representation for sign language expressions is created so that the resulting output can be directly interpreted as a sequence of signs, each containing manual and non-manual components and representing their coordination and spatial relationship. The derivation process with the constructed lexicon is presented with several examples for Korean Sign Language. We discuss implications of our proposal and future directions.

Reproducing Fairy Tales for Plot Identification

SeungJoo An and Jong C. Park
Proceedings of the 23rd Annual Conference on Human and Cognitive Language Technology (HCLT), pp. 3-8, Seoul, Korea, October 6-7, 2011.
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ํ…์ŠคํŠธ์˜ ์Šคํ† ๋ฆฌ๋ฅผ ์ž๋™์œผ๋กœ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ํ…์ŠคํŠธ์—์„œ ๊ธฐ์ˆ ๋œ ์‚ฌ๊ฑด(event)์„ ํŒŒ์•…ํ•˜๊ณ  ์ด๋“ค์„ ์กฐํ•ฉํ•˜์—ฌ ์Šคํ† ๋ฆฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š”์ง€๋ฅผ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Š” ์Šคํ† ๋ฆฌ์˜ ๊นŠ์€ ์˜๋ฏธ ๋ก ์  ์ดํ•ด๋ฅผ ์š”๊ตฌํ•˜๋Š” ๊ฒƒ ์ด์™ธ์—๋„ ํ…์ŠคํŠธ๋งˆ๋‹ค ์ƒํ™ฉ๊ณผ ์ผ์–ด๋‚˜๋Š” ์‚ฌ๊ฑด๋“ค์ด ๋‹ค์–‘ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์–ธ์–ด ์ž์›์ด ๋ถ€์กฑํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์ฒ˜๋ฆฌ์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ์‚ฌ๊ฑด๋“ค์„ ์ถ”์ƒํ™” ํ•˜์—ฌ ๋‹จ์ˆœํ•˜๊ฒŒ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค ๋ฉด ์Šคํ† ๋ฆฌ ์ดํ•ด์˜ ์ž์—ฐ์Šค๋Ÿฌ์›€์„ ์ €ํ•ดํ•˜์ง€ ์•Š๊ณ  ํ•ด๊ฒฐ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‚ฌ๊ฑด๋“ค์˜ ์ถ”์ƒํ™” ๊ณผ์ •์„ ์œ„ํ•œ ๊ธฐ์ดˆ ์—ฐ๊ตฌ๋กœ์„œ ํ…์ŠคํŠธ ์† ๋“ฑ์žฅ์ธ๋ฌผ์ด ํ–‰ํ•˜๊ฑฐ๋‚˜ ๋‹นํ•˜๋Š” ์‚ฌ๊ฑด๋“ค์„ ์ถ”์ถœํ•˜์—ฌ PMI๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์‚ฌ๊ฑด์˜ ํ๋ฆ„์„ ํŒŒ์•…ํ•˜๊ณ  ์–ธ์–ดํ•™์  ๋‹จ์„œ๋ฅผ ์ฐธ์กฐํ•˜์—ฌ ์Šคํ† ๋ฆฌ ์ดํ•ด ๊ณผ์ •์— ๋ˆ„๋ฝ๋  ์ˆ˜ ์žˆ๋Š” ์‚ฌ๊ฑด๋“ค์„ ์ถ”๊ฐ€ํ•˜์—ฌ ๋ณด์™„ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ์„ ํ†ตํ•ด ๋“ฑ์žฅ์ธ๋ฌผ์ด ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌ๊ฑด๋“ค์„ ์žฌ๊ตฌ์„ฑํ•˜์—ฌ ๋‹จ์ˆœํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค.

Reading Desk for Preschool Children and Older People with Emotional Speech Synthesis

Ho-Joon Lee, Yong-Jae Lee, and Jong C. Park
International Conference on Convergence and Hybrid Information Technology (ICHIT), LNCS 6935, pp. 740-747, Daejeon, Korea, September 23-25, 2011.
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In this paper, we introduce a reading desk designed to read books to the older people and children. For this purpose, we propose a reading desk together with an emotional speech synthesis system for Korean. The reading desk system provides a wireless audio output unit, and the reading desk is directly connected to a laptop computer in order to identify the current user and target reading material. The emotional speech synthesis system for Korean is a prosody re-synthesis system that has the option of providing four different emotions such as anger, fear, happiness, and sadness. Therefore, this system is also able to modify the speech rate and intensity information of speech as much as users want. We analyzed 240 pieces of emotional speech in order to extract distinct prosody structures for each emotion in Korean. The evaluation results show that we have achieved 48.5% of the recognition rate for happiness among four emotions, and with enough training experience, the average recognition rate has improved up to 95.5% for all emotions.

Linguistic Analysis of Picture Description for Language Impairment Diagnosis

Yong-Jae Lee, Hye-Jin Min, and Jong C. Park
Korea Computer Congress (KCC), Gyeongju, Korea, June 30-July 2, 2011.
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์‚ฌ๋žŒ์€ ์„ฑ์žฅ ๋ฐฐ๊ฒฝ์ด๋‚˜ ํ•™์Šต์— ๋”ฐ๋ผ ๊ณ ์œ ์˜ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์€ ๊ฐœ ์ธ์˜ ์–ธ์–ด ์œ ์ฐฝ์„ฑ์— ๋Œ€ํ•œ ์ง€ํ‘œ๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„์€ ์žฅ์• ์— ๋”ฐ๋ฅธ ๋ณ€ํ™”์—๋„ ๋Šฅ๋™์  ์œผ๋กœ ๋Œ€์ฒ˜ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์–ด๋–ค ํŠน์ •์ธ์˜ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ์•„์ง ๋ถ€์กฑํ•œ ์‹ค์ • ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐœ์ธ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ ํŒŒ์•…์„ ์œ„ํ•˜์—ฌ ์ผ์ฐจ์ ์œผ๋กœ ์ผ๋ฐ˜์ธ๋“ค์˜ ๊ทธ๋ฆผ ์„ค๋ช…๊ธ€ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ์•˜์œผ๋ฉฐ, ์ด์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์–ธ์–ด ์žฅ์•  ์ง„๋‹จ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ  ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋กœ ํ˜•ํƒœ์†Œ ๋‹จ์œ„, ๋‹จ์–ด ๋‹จ์œ„, ๊ทธ๋ฆฌ๊ณ  ๋‚ด์šฉ ์ „๋‹ฌ์˜ ๋ฐฉ์‹์— ๋”ฐ๋ฅธ ๊ฐœ์ธ์˜ ์–ธ์–ด ์‚ฌ์šฉ ํŠน์„ฑ์„ ์ผ๋ถ€ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด์™€ ๊ฐ™์€ ํŠน์„ฑ์€ ํ–ฅํ›„ ์น˜๋งค์™€ ๊ฐ™์€ ์ธ์ง€ ๊ธฐ๋Šฅ ์žฅ์• ๋กœ ์ธํ•œ ์–ธ์–ด ์‚ฌ์šฉ ์˜ ๋ณ€ํ™”๋ฅผ ์ถ”์ ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์‹ค๋งˆ๋ฆฌ๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.

Improving Accessibility to Web Documents for the Aurally Challenged with Sign Language Animation

Jin-Woo Chung, Ho-Joon Lee, and Jong C. Park
International Conference on Web Intelligence, Mining and Semantics (WIMS'11), Sogndal, Norway, May 25-27, 2011.
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In this paper, we describe how to improve accessibility for the aurally challenged in a web environment, focusing on utilizing a signing avatar for web pages. Many systems were previously proposed to make a web environment more accessible for the deaf people by providing signed expressions, i.e. translating written text into sign language animations and presenting them in a proper way, based on the observation that deaf users normally have much difficulty understanding text-based information as well as audio contents. We analyze the strengths and weaknesses of these systems with respect to discussed design criteria, and propose a system that presents a signing avatar for web page documents via a mobile device, which is expected to overcome the shortcomings of the previous systems and to improve the accessibility of deaf users to textual contents in a web environment. The proposed system has three main parts based on a client-server architecture: 1) a client that executes a web browser and transmits selected text to the server, 2) a server that takes text as input and translates it into signed expressions through a sign language generation module, and 3) a mobile device that displays signing animation transmitted from the server by streaming. We also present some linguistic issues raised by the difference between Korean and Korean Sign Language. To the best of our knowledge, this is the first approach to the use of a mobile device for web document access by the aurally challenged people. We discuss implications of our study and future directions.

Physical Push with a Socially Intelligent Robot: Make your wishes to 'Genie in the Lamp'

Hye-Jin Min and Jong C. Park
Proceedings of the 6th IEEE/ACM International Conference on Human-Robot Interaction, Late Breaking News, pp. 203-204, March 6-9, 2011, Lausanne, Switzerland. ACM
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This paper proposes a robotic agent named โ€˜Genieโ€™ that understands a userโ€™s wish and gives its possible answers on a social network platform. Once a potential wish is detected upon monitoring the text updates in the micro-blog of the user, the agent initiates a task to help the user with both NLP and metadata analysis. As an interaction scenario, we set the type of a robot as an agent that identifies wishful products by searching for and analyzing product information on the web. After an analysis of the vast amount of data, the agent provides possible answers to the user as a way of granting the wish that might require additional time and effort to achieve. In order to draw the user's attention, the agent makes a physical movement as a push notification with more user-friendliness.

Annotation of Protein State Information in Biomedical Text

Hee-Jin Lee and Jong C. Park
9th Asia Pacific Bioinformatics Conference (APBC), Poster Presentation, Incheon, Korea, January 11-14, 2011.

Korean Speech Synthesis for Automatic Fairy Tale Narration with Automatic Identification of Character Roles

SeungJoo An, Ho-Joon Lee, and Jong C. Park
HCI Conference Korea, Alpensia resort, January 26-28, 2011.
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๋ถ€๋ชจ๋“ค์ด ๋ชจ๋‘ ์ผ์„ ํ•˜์—ฌ ์•„์ด๋“ค์ด ํ˜ผ์ž ์žˆ๋Š” ์‹œ๊ฐ„์ด ๋Š˜์–ด๋‚˜๊ฒŒ ๋จ์— ๋”ฐ๋ผ ์•„์ด๋“ค์—๊ฒŒ ํ•„์š”ํ•œ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ์‹œ์Šคํ…œ์ด ํ•„์š”ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด ์ค‘์—์„œ ์ž๋™ ๋™ํ™” ๊ตฌ์—ฐ ์‹œ์Šคํ…œ์€ ์•„์ด๋“ค์˜ ์–ธ์–ด ๋Šฅ๋ ฅ๊ณผ ์ •์„œ ๋ฐœ๋‹ฌ์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ์ด ๋•Œ, ๋™ํ™” ์† ๋“ฑ์žฅ ์ธ๋ฌผ์˜ ์—ญํ• ์ด ์ œ๋Œ€๋กœ ํŒ๋‹จ๋˜์ง€ ๋ชปํ•œ๋‹ค๋ฉด ๋™ํ™”๊ฐ€ ์ „๋‹ฌํ•˜๊ณ ์ž ํ•˜๋Š” ์˜๋ฏธ์™€ ๋‹ค๋ฅด๊ฒŒ ๋™ํ™” ๋‚ด์šฉ์„ ๋ฐœํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋™ํ™” ์† ๋“ฑ์žฅ์ธ๋ฌผ์˜ ์—ญํ• ์„ ๋ถ„๋ฅ˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋‹ค๋ฃจ์–ด์•ผ ํ•  ์–ธ์–ด์  ์š”์†Œ๋“ค์„ ํ†ตํ•˜์—ฌ ๋™ํ™” ์† ๋“ฑ์žฅ์ธ๋ฌผ์˜ ์ž๋™ ์—ญํ•  ๋ถ„๋ฅ˜ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋ ‡๊ฒŒ ๋ถ„๋ฅ˜๋œ ์—ญํ• ์— ๋”ฐ๋ผ์„œ ์ ์ ˆํ•œ ์Œ์„ฑ ํ•ฉ์„ฑ์„ ํ†ตํ•˜์—ฌ ๋ณด๋‹ค ๋™ํ™”์˜ ์˜๋ฏธ ์ „๋‹ฌ์ด ๋ถ„๋ช…ํ•œ ์ž์—ฐ์Šค๋Ÿฌ์šด ์Œ์„ฑ ํ‘œํ˜„์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์Œ์„ฑ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค.
As there is a growing tendency where parent leave their children alone for their work, a system which provides necessary services to children is needed. Among these services, an automatic fairy tale narration system can help language and emotional development of young children. However, if roles of the characters in the story cannot be determined correctly by an automatic fairy tale narration system, the meaning of fairy tales can be conveyed differently, if not distorted. In this paper, we propose an automatic role identification system based on linguistic clues to classify such roles, and through such classified roles, a speech synthesis system for more natural and clear automatic fairy tale narration.

Evaluation of Emotion Categories based on the Analysis of Emotion-Rich Fairy Tales

Ho-Joon Lee and Jong C. Park
HCI Conference Korea, Alpensia resort, January 26-28, 2011.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ „๋ž˜ ๊ตฌ์—ฐ ๋™ํ™”๋ฅผ ๋ถ„์„ํ•˜์—ฌ, ๋ฐœํ™”๋ฌธ์— ๋Œ€ํ•œ ๊ฐ์ • ์ƒํƒœ๊ฐ€ ๋ช…์‹œ์ ์œผ๋กœ ํ‘œํ˜„๋œ ๋ฌธ์žฅ์„ ์ถ”์ถœํ•˜๊ณ , ์ถ”์ถœ๋œ ๊ฐ์ • ์ƒํƒœ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ์ • ๋ฒ”์ฃผ์˜ ๋ถ„ํฌ๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ, ์ „๋ž˜๊ตฌ์—ฐ ๋™ํ™”์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ์ • ๋ฒ”์ฃผ์˜ ํŠน์„ฑ์„ ๋ถ„์„ํ•œ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํ™”๋‚จ๊ณผ ๋†€๋žŒ์˜ ๊ฐ์ •์€ ๋‹ค๋ฅธ ๊ฐ์ •์— ๋น„ํ•ด ๋‹จ์ผํ™”๋œ ํ˜•ํƒœ๋กœ ํ‘œํ˜„๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ตœ์ข…์ ์œผ๋กœ ์ด๋Ÿฌํ•œ ์ •๋ณด๊ฐ€ ๊ฐ์ • ํ•ฉ์„ฑ์ด๋‚˜ ๊ฐ์ • ์ธ์‹ ๊ณผ์ •์—์„œ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์ธ๋‹ค.
In this paper, we analyze the characteristics of emotion categories derived from the utterances of fairy tales. For this purpose, we extract explicit emotional states of each utterance, and calculate their distributions. As a result, we find that the emotional state of anger and astonishment are well-defined emotion categories, whereas other need more refinement. This finding can be used for the improvement of emotional speech synthesis and recognition systems.

Automatic Identification of Character Roles for Natural Fairy Tale Narration

SeungJoo An and Jong C. Park
KIISE Fall Conference, Danguk University, November 5-6, 2010.
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๋™ํ™”๋ฅผ ๊ตฌ์—ฐํ•  ๋•Œ ๊ตฌ์—ฐ์ž๋Š” ๋™ํ™” ์† ๋“ฑ์žฅ ์ธ๋ฌผ์˜ ์—ญํ• ์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ์ •์„ ์‹ค์–ด ๋ฐœํ™”ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋…์ž์ธ ์œ ์•„๋“ค์˜ ๊ด€์‹ฌ์„ ์œ ๋ฐœํ•˜๊ณ  ๋ชฐ์ž…์‹œํ‚ด์œผ๋กœ์จ, ์ดํ•ด๋„๋ฅผ ๋†’์ธ๋‹ค. ์ด์™€ ๊ฐ™์ด ๋™ํ™” ์† ์ธ๋ฌผ์˜ ์—ญํ• ์— ๋Œ€ํ•œ ์ ์ ˆํ•œ ์ดํ•ด๋Š” ์ž๋™ ๋™ํ™” ๊ตฌ์—ฐ์— ์žˆ์–ด์„œ ์ค‘์š”ํ•œ ์š”์†Œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋™ํ™” ์† ๋“ฑ์žฅ์ธ๋ฌผ์˜ ์—ญํ• ์„ ๋ถ„๋ฅ˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋‹ค๋ฃจ์–ด์•ผ ํ•  ์–ธ์–ด์  ์š”์†Œ๋“ค์— ๋Œ€ํ•˜์—ฌ ์‚ดํŽด๋ณธ๋‹ค. ๋˜ํ•œ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋Ÿฌํ•œ ์—ญ ํ• ์„ ์ž๋™์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๊ณ , ์ฒ˜๋ฆฌํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์ œ์‹œํ•œ๋‹ค.

A Ubiquitous Smart Parenting and Customized Education Service Robot

Ho-Joon Lee and Jong C. Park
The 2010 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), 2010.
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In this paper, we introduce a u-SPACE service robot, designed to help children who may be left alone while their caregivers are away from home. In order to protect children from indoor dangers, this service robot provides customized guiding messages taking into account the location information and behavioral patterns of a child, after the detection of dangerous objects and situations. And these guiding messages are vocalized by our emotional speech generation system. This emotional speech generation system is also being put to use in reading fairy tales to a child, as a part of a home education service. The outward appearance of the u-SPACE service robot is modeled on a teddy bear, in order to provide a safe and comforting environment for children. Two touch sensors designed for basic interactions between a child and the robot are installed on each hand of the robot, and an RFID tag is placed inside the body. A PDA with a Wi-Fi communication module, a touch screen, and a speaker is used as a main operating device of this u-SPACE service robot.

Detecting and Resolving Syntactic Ambiguity for Automatic Korean-Korean Sign Language Translation

Jin-Woo Chung and Jong C. Park
Proceedings of the 22nd Annual Conference on Human and Cognitive Language Technology, pp. 55-62, 2010.
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์ˆ˜ํ™”๋Š” ๋†์ธ ์‚ฌํšŒ์—์„œ ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์‹œ๊ฐ์–ธ์–ด๋กœ์„œ ์Œ์„ฑ์–ธ์–ด์ธ ํ•œ๊ตญ์–ด์™€ ํ†ต์‚ฌ์ ์ธ ์ธก๋ฉด์—์„œ ๋งŽ์€ ์ฐจ์ด๋ฅผ ๋ณด์ธ๋‹ค. ํŠนํžˆ ์ˆ˜ํ™”์—์„œ๋Š” ์กฐ์‚ฌ์™€ ์–ด๋ฏธ๊ฐ€ ๊ฑฐ์˜ ์‚ฌ์šฉ๋˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์—์„œ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋Œ€๋กœ ์ด ๋“ค์„ ์ œ๊ฑฐํ•œ ํ›„ ์–ด์ˆœ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ์ฑ„ ๋ฌธ์žฅ ์„ฑ๋ถ„์˜ ๊ธฐ๋ณธํ˜•์„ ๊ทธ๋Œ€๋กœ ๋‚˜์—ดํ•˜์—ฌ ์ˆ˜ํ™”๋ฌธ์„ ์ƒ์„ฑํ•  ๊ฒฝ์šฐ ๋ฌธ์žฅ ์„ฑ๋ถ„ ๊ฐ„์˜ ํ†ต์‚ฌ์  ๊ด€๊ณ„๊ฐ€ ์• ๋งคํ•ด์งˆ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ†ต์‚ฌ์  ์ค‘์˜์„ฑ์ด ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์„ ์ˆ˜ํ™”๋ฌธ ์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณผ์ •์—์„œ ์ถ”๊ฐ€์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ฒŒ ๋˜๋Š” ํŠน์ • ํ†ต์‚ฌ๊ตฌ์กฐ์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ , ์ด๋Ÿฌํ•œ ํ†ต์‚ฌ๊ตฌ์กฐ๋ฅผ ๊ธฐ๋ณธ๋…ผํ•ญ๊ตฌ์กฐ, ํ•œ์ •์ˆ˜์‹๊ตฌ์กฐ, ๋ณ‘๋ ฌ๊ตฌ์กฐ, ์„œ์ˆ ๊ตฌ์กฐ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๊ฐ๊ฐ์„ ํŒŒ์•…ํ•˜๊ณ  ๊ทธ์— ๋”ฐ๋ผ ํ†ต์‚ฌ ์  ์ค‘์˜์„ฑ์„ ํ•ด์†Œํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค.

Intonation Generation for Korean Speech Synthesis with Automated Sentence Type Classification

Jin-Woo Chung, Ho-Joon Lee, and Jong C. Park
21th HCI Conference Korea, Phoenix Park, January 27-29, 2010.
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์Œ์„ฑ์€ ์ธ๊ฐ„๊ณผ ์ธ๊ฐ„ ์‚ฌ์ด์˜ ์ƒํ˜ธ ์ž‘์šฉ์—์„œ ๊ฐ€์žฅ ๊ธฐ๋ณธ์ ์ธ ์ •๋ณด ์ „๋‹ฌ ๋ฐฉ์‹์ด๋ฉฐ ์ตœ๊ทผ ๋“ค์–ด ๋กœ๋ด‡์„ ํฌํ•จํ•œ ์ธ๊ฐ„๊ณผ ๊ธฐ๊ณ„ ์‚ฌ์ด์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์ƒํ˜ธ์ž‘์šฉ์„ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์ˆ˜๋‹จ์œผ๋กœ๋„ ๋„๋ฆฌ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์Œ์„ฑ์€ ๋ฌธ์ž ํ˜•ํƒœ์˜ ์–ธ์–ด ํ‘œํ˜„์ด ์†Œ๋ฆฌ ์ •๋ณด๋กœ ๋ณ€ํ™˜๋œ ๊ฒƒ์œผ๋กœ ์–ต์–‘ ์ •๋ณด๋ฅผ ํฌํ•จํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ์–ต์–‘ ์ •๋ณด๊ฐ€ ์ ์ ˆํžˆ ํ‘œํ˜„๋˜์ง€ ๋ชปํ•œ๋‹ค๋ฉด ๋ฌธ์ž๊ฐ€ ์ง€๋‹Œ ์ •๋ณด๋งˆ์ € ์˜จ์ „ํ•˜๊ฒŒ ์ „๋‹ฌํ•˜๊ธฐ ์–ด๋ ค์šฐ๋ฏ€๋กœ ์ƒํ™ฉ์— ๋งž๋Š” ์–ต์–‘ ์ •๋ณด๋ฅผ ํ‘œํ˜„ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ํ•œ๊ตญ์–ด ์Œ์„ฑ์—์„œ ๋ฌธ์žฅ์˜ ์ „์ฒด์ ์ธ ์–ต์–‘์€ ๊ทธ ๋ฌธ์žฅ์˜ ์œ ํ˜•์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚˜๋ฏ€๋กœ, ์ž์—ฐ์Šค๋Ÿฌ์šด ์Œ์„ฑ ํ•ฉ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ๋ฌธ์žฅ์˜ ์œ ํ˜•์„ ์ž˜ ํŒŒ์•…ํ•ด์•ผ ํ•œ๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์˜ ์œ ํ˜•์„ ์ž๋™์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฌธํ˜• ๋ถ„๋ฅ˜ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋ ‡๊ฒŒ ๋ถ„๋ฅ˜๋œ ๋ฌธ์žฅ ์œ ํ˜•์— ๋งž๋Š” ์–ต์–‘ ์ •๋ณด๋ฅผ ์ƒ์„ฑํ•˜์—ฌ ์ž์—ฐ์Šค๋Ÿฌ์šด ์Œ์„ฑ ํ‘œํ˜„์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์Œ์„ฑ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค.

Automatic Extraction of the Usage Information from the Component Words in Gene Ontology Terms to Enhance Consistency and Predictability

Seung-Cheol Baek and Jong C. Park
3rd International Symposium on Languages in Biology and Medicine (LBM 2009), long paper, Seogwipo, Korea, November 8-10, 2009.
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The Gene Ontology (GO) is a controlled vocabulary that has gone through constant changes, motivated primarily by the need to reflect the dynamic nature of knowledge it addresses and the need for usability improvement. A good policy on such changes would be to maintain consistency across terms and structures so as to highlight the missing parts that are likely to be added afterwards, or the unchanged parts to which a policy on usability improvement might not have yet applied. In particular, we argue that the component words inside terms must be used consistently across terms, in order to enhance the predictability of such terms, thus their usability as well. For this purpose, we propose a representation for word usage and a method for extracting it from GO and show its utility in identifying the direction of future changes readily as well as in enhancing the consistency of terms.

Synchronization of Manual and Non-Manual Signals in Automatic Generation of Sign Language Expressions

SangYoon Jung, Eunyoung Chang, and Jong C. Park
Proceedings of the 21th Annual Conference on Human and Cognitive Language Technology (HCLT 2009), pp. 81-86, October. 2009.
(selected as best paper)
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๋น„์ˆ˜์ง€ ์‹ ํ˜ธ๋Š” ์ˆ˜ํ™”๋ฅผ ํ†ตํ•œ ์˜์‚ฌ์†Œํ†ต์„ ํ•˜๋Š” ๊ณผ์ •์— ์ˆ˜์ง€ ์‹ ํ˜ธ ๋ชป์ง€์•Š๊ฒŒ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ˆ˜์ง€ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ๋น„ํ•ด ์•„์ง๊นŒ์ง€ ๋งค์šฐ ๋ถ€์กฑํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ์˜ ํŠน์ง•์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ๋ฅผ ์ˆ˜์ง€ ์‹ ํ˜ธ์™€ ํ•จ๊ป˜ ์žฌํ˜„ํ•˜๋Š” ๊ณผ์ •์—๋Š” ์ •ํ™•๋„ ๋ฌธ์ œ์™€ ๋™๊ธฐํ™” ๋ฌธ์ œ๊ฐ€ ์žˆ๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋™๊ธฐํ™” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ๊ตฌํ˜„๋œ ์‹œ์Šคํ…œ์€ ์ž…๋ ฅ๋œ ๋ฌธ์žฅ์„ ๊ตฌ๋ฌธ ๋ถ„์„ํ•˜์—ฌ ์ˆ˜์ง€ ์‹ ํ˜ธ์™€ ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋ถ€๋ถ„๊ณผ ๊ตฌ๋ฌธ ๋ถ„์„๋œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜ํ™” ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์œ„ํ•œ ์•ก์…˜ ์Šคํฌ๋ฆฝํŠธ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋‰œ๋‹ค. ์ˆ˜์ง€ ์‹ ํ˜ธ์™€ ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ์˜ ์—ฐ๊ฒฐ ์ˆœ์„œ์™€ ๋ฐฉ์‹์— ๋”ฐ๋ผ ์ˆ˜ํ™”์˜ ๋œป์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃจ๋Š” ๋น„์ˆ˜์ง€ ์‹ ํ˜ธ์˜ ๋™๊ธฐํ™” ๋ฌธ์ œ๋Š” ์ˆ˜ํ™” ์ž๋™ ์ƒ์„ฑ์— ์žˆ์–ด์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค.

Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses

Hye-Jin Min and Jong C. Park
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 169-172, Singapore, August 2-7, 2009.
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We propose categories of finer-grained polarity for a more effective aspect-based sentiment summary, and describe linguistic and ontological clues that may affect such fine-grained polarity. We argue that relevance for satisfaction, contrastive weight clues, and certain adverbials work to affect the polarity, as evidenced by the statistical analysis.

Interpretation of User Evaluation for Emotional Speech Synthesis System

Ho-Joon Lee and Jong C. Park
13th International Conference on Human-Computer Interaction (HCII 2009), San Diego, USA, July 19-24, 2009.
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Whether it is for human-robot interaction or for human-computer interaction, there is a growing need for an emotional speech synthesis system that can provide the required information in a more natural and effective manner. In order to identify and understand the characteristics of basic emotions and their effects, we propose a series of user evaluation experiments on an emotional prosody modification system that can express either perceivable or slightly exaggerated emotions classified into anger, joy, and sadness as an independent module for a general purpose speech synthesis system. In this paper, we propose two experiments to evaluate the emotional prosody modification module according to different types of the initial input speech. And we also provide a supplementary experiment to understand the apparently prosody-independent emotion, or joy, by replacing the resynthesized joy speech information with original human voice recorded in the emotional state of joy.

Analysis and Computational Processing of Homonyms in Korean for Automatic Sign Language Generation

SangYoon Jung, Eunyoung Chang, and Jong C. Park
Proceedings of the Korea Computer Congress (KCC 2009), Vol. 36, No. 1(C), pp. 315-320, Jeju, July 1-3, 2009.
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ํ•œ๊ตญ์–ด๋ฅผ ์ˆ˜ํ™”๋กœ ์ž๋™ ์ƒ์„ฑํ•˜๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์–ด๊ฐ€ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋Š” ๊ฐ ๊ฐœ๋…์— ์–ด์šธ๋ฆฌ๋Š” ์ˆ˜ํ™”๋™์ž‘์„ ๋ฏธ๋ฆฌ ๋งŒ๋“ค์–ด ๋†“๊ณ  ์ด๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์—ฐ๊ฒฐ์‹œํ‚ด์œผ๋กœ์จ ์ˆ˜ํ™” ํ‘œํ˜„์„ ์ž๋™ ์ƒ์„ฑํ•˜๋ ค ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ํ•œ๊ตญ์–ด ๋™์Œ์ด์˜์–ด์— ๋Œ€ํ•œ ์ˆ˜ํ™”๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒฝ์šฐ์— ์ด์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ด๋Š” ๊ฑด์ฒญ์ธ์ด ์ƒ๊ฐํ•˜๋Š” ํ•˜๋‚˜์˜ ๊ฐœ๋…์ด ๋†์ธ์ด ์‚ฌ์šฉํ•˜๋Š” ์ˆ˜ํ™”์—์„œ๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋‹ค๋ฅธ ํ˜•ํƒœ๋กœ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์™€ ๊ฐ™์ด ๊ฑด์ฒญ์ธ๋“ค ์‚ฌ์ด์—์„œ ํ•˜๋‚˜์˜ ๊ฐœ๋…์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ๋‹จ์–ด๋ฅผ ๋†์ธ๋“ค์ด ์—ฌ๋Ÿฌ ๋‹ค๋ฅธ ํ˜•ํƒœ๋กœ ํ‘œํ˜„ํ•˜๋Š” ๊ฒฝ์šฐ ๊ธฐ์กด์˜ ์ˆ˜ํ™” ์ž๋™ ์ƒ์„ฑ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค๋Š” ์ ์„ ๋ณด์™„ํ•œ ์ˆ˜ํ™” ์ž๋™ ์ƒ์„ฑ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค.

Extracting Melodies from Piano Solo Music Based on Characteristics of Music

Yoonjae Choi and Jong C. Park
Proceedings of the Korea Computer Congress (KCC 2009), Vol. 36, No. 1(A), pp. 124-125, Jeju, July 1-3, 2009.
(selected as best paper)
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์ธํ„ฐ๋„ท์˜ ๋ฐœ๋‹ฌ๋กœ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์ž๋ฃŒ์˜ ๊ฒ€์ƒ‰ ๋ฐ ํ™œ์šฉ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋””์ง€ํ„ธ ์Œ๋ฐ˜ ์‹œ์žฅ์˜ ๋น ๋ฅธ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด ์Œ์•… ๊ฒ€์ƒ‰ ๋ฐ ์ถ”์ฒœ์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ๊ณ„์†ํ•ด์„œ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š”๋ฐ ์ด๋Ÿฌํ•œ ์„œ๋น„์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์Œ์•… ๊ธฐ๋ฐ˜ ์‘์šฉ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ๋Š” ์ผ๋ฐ˜์ ์ธ ์Œ์•…์˜ ํ˜•ํƒœ์ธ ๋‹ค์Œ(Polyphonic) ์Œ์•…์—์„œ ๋ฉœ๋กœ๋””๋ฅผ ์ถ”์ถœํ•˜๋Š” ๊ณผ์ •์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์Œ์˜ ๋ณต์žก๋„๊ฐ€ ๋†’๊ณ  ๋„“์€ ์Œ์—ญ์„ ๊ฐ€์ง€๋Š” ์Œ์•…์„ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋Š” ํ”ผ์•„๋…ธ ์†”๋กœ ์Œ์•…์—์„œ ๋ฉœ๋กœ๋””๋ฅผ ์ถ”์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค.

Extracting Melodies from Polyphonic Piano Solo Music Based on Patterns of Music Structure

Yoonjae Choi, Ho-Joon Lee, Hodong Lee, and Jong C. Park
Proceedings of the 20th Human Computer Interaction (HCI 2009), pp. 725-732, Phoenix Park, Feb 9-11, 2009.
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Thanks to the development of the Internet, people can easily access a vast amount of music. This brings attention to application systems such as a melody-based music search service or music recommendation service. Extracting melodies from music is a crucial process to provide such services. This paper introduces a novel algorithm that can extract melodies from piano music. Since piano can produce polyphonic music, we expect that by studying melody extraction from piano music, we can help extract melodies from general polyphonic music.

Analysis and Use of Intonation Features for Emotional States

Ho-Joon Lee and Jong C. Park
Proceedings of the 20th Annual Conference on Human and Cognitive Language Technology, pp. 144-149, October 11-12, 2008.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 8๊ฐœ์˜ ๋ฌธ์žฅ์— ๋Œ€ํ•ด์„œ 6๋ช…์˜ ํ™”์ž๊ฐ€ 5๊ฐ€์ง€ ๊ฐ์ • ์ƒํƒœ๋กœ ๋ฐœํ™”ํ•œ ์ด 240๊ฐœ์˜ ๋ฌธ์žฅ์„ ๊ฐ์ • ์Œ์„ฑ ๋ง๋ญ‰์น˜๋กœ ํ™œ์šฉํ•˜์—ฌ ๊ฐ ๊ฐ์ • ์ƒํƒœ์—์„œ ํŠน์ง•์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ์–ต์–‘ ํŒจํ„ด์„ ๋ถ„์„ํ•˜๊ณ , ์ด๋Ÿฌํ•œ ์–ต์–‘ ํŒจํ„ด์„ ์Œ์„ฑ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์— ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ๋…ผ์˜ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ์ • ์ƒํƒœ์— ๋”ฐ๋ฅธ ํŠน์ง•์  ์–ต์–‘ ํŒจํ„ด์„ ์–ต์–‘๊ตฌ์˜ ๊ธธ์ด, ์–ต์–‘๊ตฌ์˜ ๊ตฌ๋ง ๊ฒฝ๊ณ„ ์„ฑ์กฐ, ํ•˜๊ฐ• ํ˜„์ƒ์— ์ค‘์ ์„ ๋‘์–ด ๋ถ„์„ํ•˜๊ณ , ๊ธฐ์จ, ์Šฌํ””, ํ™”๋‚จ ๊ณตํฌ์˜ ๊ฐ์ •์„ ๊ตฌ๋ถ„ ์ง€์„ ์ˆ˜ ์žˆ๋Š” ์–ต์–‘ ํŠน์ง•๋“ค์„ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์— ์ ์šฉํ•˜๋Š” ๊ณผ์ •์„ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™”๋‚จ์˜ ๊ฐ์ •์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์–ต์–‘์˜ ์ƒ์Šน ํ˜„์ƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ๊ฐ ๊ฐ์ •์— ๋”ฐ๋ฅธ ํŠน์ง•์  ์–ต์–‘ ํŒจํ„ด์„ ์ฐพ์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค.

Towards Knowledge Discovery through Automatic Inference with Text Mining in Biology and Medicine

Hee-Jin Lee and Jong C. Park
3rd International Symposium on Semantic Mining in Biomedicine (SMBM), Turku, Finland, September 1-3, 2008.
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Field experts in biology and medicine search the literature for state-of-the-art results and occasionally discover knowledge through manual inference on published causal relations. However, the results of such inference cannot be sufficiently accurate and/or complete, as the domain of published relations is rather huge. In this paper, we introduce an automatic inference system, BioDetective, which works on literature-mined qualitative causal information in biology and medicine. BioDetective provides proofs for such qualitative causal information, and predicts the existence of new causal information, if there is any. The system is tested with a case study, where literature-mined information about protein regulation is utilized to come up with new knowledge.

An effective way to learn biological knowledge with linguistic resources

Jin-Bok Lee, Tak-eun Kim, and Jong C. Park
18th International Congress of Linguists (CIL 18), Seoul, Korea, July 21-26, 2008.
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The most general and effective way for people to acquire desired knowledge is to learn from tutors with face-to-face contact. The tutors can pick out important pieces of information and deliver them systematically to the learners considering their specialties, interests, rates of progress, and so on. However, since all learners may not be taught by tutors during their convenient time, the field of e-learning or distance learning has been emerged.
To maintain the benefits of face-to-face learning in an automatic way, the challenge remains in equipping computers with the expertise, skills and modes of actions of the human tutor, overcoming spatial, temporal, ocio-economical and environmental restrictions. In order to overcome these challenges, we focus on two issues: (1) information investigation: how to pick out essential pieces of information that do not include overlapping or obsolete pieces, and (2) information delivery: how to deliver the selected ones to learners effectively in point of understanding and memorization.
In this paper, we propose a web-based smart tutoring system for helping biology-major student to learn genes. To incorporate the two issues described above into our tutoring system, we extensively use linguistic resources in the biology domain, such as Gene Ontology or UMLS, for selecting and classifying information from huge amount of data. We believe that our tutoring system can autonomously carry out almost all the functionalities of human tutor including investigation, delivery, and adaptation of learnerโ€™s feedbacks.

Syntactic Construction of Coordination in Sign Language Generation

Hodong Lee, Sangha Kim, and Jong C. Park
18th International Congress of Linguists (CIL 18), Seoul, Korea, July 21-26, 2008.
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Coordination in sign languages is an essential construction to describe more than one kind of information, as used in natural languages. Although it may appear to follow general rules of coordination, its realization with multi-channel motions is often quite different from that in natural languages, due to the differences at levels of syntax and semantics. A multi-channel motion is simultaneously composed of shape, position, orientation and movement of the hands, arms, body, or face. In this paper, we address the problems in converting coordination-bearing sentences into their matching motions in sign languages. In particular, we focus on the issues between the Korean language and the Korean sign language (KSL).

Sign Language Generation with Animation by Adverbial Phrase Analysis

Sangha Kim and Jong C. Park
17th Human Computer Interaction (HCI 2008), Phoenix Park, Feb 13-15, 2008.
(selected as best paper)
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Sign languages, commonly used in aurally challenged communities, are a kind of visual language expressing sign words with motion, Spatiality and motility of a sign language are conveyed mainly via sign words as predicates. A predicate is modified by an adverbial phrase with an accompanying change in its semantics so that the adverbial phrase can also affect the overall spatiality and motility of expressions of a sign language. In this paper, we analyze the semantic features of adverbial phrases which may affect the motion-related semantics of a predicate in converting expressions in Korean into those in a sign language and propose a system that generates corresponding animation by utilizing these features.

On the Automatic Generation of Illustrations for Events in Storybooks: Representation of Illustrative Events

Seung-Cheol Baek, Hee-Jin Lee, and Jong C. Park
17th Human Computer Interaction (HCI 2008), Phoenix Park, Feb 13-15, 2008.
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Storybooks, especially those for children, may contain illustrations. An automated system for generating illustrations would help the production process of storybook publishing. In this paper, we propose a method for automatically generating layouts of objects during generating illustrations. In generated layouts, it is preferred to avoid unnecessary overlap between objects, corresponding to the spatial information in storybooks. We first define a representation scheme for spatial information in natural language sentences using tree structures and predicate-argument structures. Unification of tree structures and Region Connection Calculus are then used to manipulate the information and generate corresponding illustrations.

Visualizing the Temporal Distribution of Terminologies for Biological Ontology Development

Tak-eun Kim, Hodong Lee, Jinah Park, and Jong C. Park
International Conference on Visualization and Data Analysis (VDA), San Jose, USA, 26-31 January, 2008.
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Communities in biology have developed a number of ontologies that provide standard terminologies for the characteristics of various concepts and their relationships. However, it is difficult to construct and maintain such ontologies in biology, since it is a non-trivial task to identify commonly used potential member terms in a particular ontology, in the presence of constant changes of such terms over time as the research in the field advances. In this paper, we propose a visualization system, called BioTermViz, which presents the temporal distribution of ontological terms from the text of published journal abstracts. BioTermViz shows such a temporal distribution of terms for journal abstracts in the order of published time, occurrences of the annotated Gene Ontology concepts per abstract, and the ontological hierarchy of the terms. With a combination of these three types of information, we can capture the global tendency in the use of terms, and identify a particular term or terms to be created, modified, segmented, or removed, effectively developing biological ontologies in an interactive manner. In order to demonstrate the practical utility of BioTermViz, we describe several scenarios for the development of an ontology for a specific sub-class of proteins, or ubiquitin-protein ligases.

Analysis of Indirect Uses of Interrogative Sentences Carrying Anger

Hye-Jin Min and Jong C. Park
PACLIC 21, Seoul National University, November 1-3, 2007.
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Interrogative sentences are generally used to perform speech acts of directly asking a question or making a request, but they are also used to convey such speech acts indirectly. In the utterances, such indirect uses of interrogative sentences usually carry speakerโ€™s emotion with a negative attitude, which is close to an expression of anger. The identification of such negative emotion is known as a difficult problem that requires relevant information in syntax, semantics, discourse, pragmatics, and speech signals. In this paper, we argue that the interrogatives used for indirect speech acts could serve as a dominant marker for identifying the emotional attitudes, such as anger, as compared to other emotion-related markers, such as discourse markers, adverbial words, and syntactic markers. To support such an argument, we analyze the dialogues collected from the Korean soap operas, and examine individual or cooperative influences of the emotion-related markers on emotional realization. The user study shows that the interrogatives could be utilized as a promising device for emotion identification.

On the Automatic Generation of Illustrations for Events in Storybooks

Seung-Cheol Baek, Eunyoung Chang, and Jong C. Park
KIISE 2007 Fall Conference, Pusan National University, October 26-27, 2007.
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๋ฌธํ•™๊ฐ€์™€ ์ผ๋ฐ˜์ธ๋“ค ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„๊ฐ€ ์ธํ„ฐ๋„ท ์†Œ์„ค ๋“ฑ์œผ๋กœ ํฌ๋ฏธํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์–ด๋ฆฐ์ด๋ฅผ ๋…์ž๋กœ ๊ฒฐ์ •ํ•˜๊ณ  ์ž‘ ํ’ˆ์„ ์ฐฝ์ž‘ํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์€ ์‚ฝํ™”๋ฅผ ๊ทธ๋ ค์„œ ์ž‘ํ’ˆ์„ ์ถœํŒํ•˜๊ณ  ์‹ถ์–ดํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์šฉ์ž๊ฐ€ ๋™ํ™”์˜ ํŠน์ • ์‚ฌ ๊ฑด์„ ์ฃผ์ œ๋กœ ์‚ฝํ™”๋ฅผ ์ƒ์„ฑํ•˜๊ณ ์ž ํ•  ๋•Œ ์ด๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŠนํžˆ ๋ฌธ์žฅ๋“ค์˜ ๊ฒฐํ•ฉ์œผ๋กœ ํ‘œํ˜„๋˜๋Š” ํ•˜๋‚˜์˜ ์‚ฌ๊ฑด์„ ์‚ฝํ™”๋กœ ๊ทธ๋ฆฌ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž์—ฐ์–ธ ์–ด๋ฅผ ํ•ด์„ํ•˜์—ฌ ์‚ฌ๊ฑด์„ ์ถ”์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฒฐํ•ฉ ๋ฒ”์ฃผ ๋ฌธ๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค.

Translating a Complex Sentence in Korean into a Sign Language Script for an Automatic Sign Language Generation

Sangha Kim, Eunyoung Chang, and Jong C. Park
the 19th Annual Conference on Human and Cognitive Language Technology (KLIP 2007), Kyungpook National University, October 12-13, 2007.

Characteristics of Spoken Discourse Markers and their Application to Speech Synthesis Systems

Ho-Joon Lee and Jong C. Park
the 19th Annual Conference on Human and Cognitive Language Technology (KLIP 2007), Kyungpook National University, October 12-13, 2007.

Customized Message Generation and Speech Synthesis in Response to the Characteristic Behavioral Patterns of Children

Ho-Joon Lee and Jong C. Park
HCI International, Beijing, P. R. China, July 22-27, 2007.
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There is a growing need for a user-friendly human-computer interaction system that can respond to various characteristics of a user in terms of behavioral patterns, mental state, and personalities. In this paper, we present a system that generates appropriate natural language spoken messages with customization for user characteristics, taking into account the fact that human behavioral patterns usually reveal oneโ€™s mental state or personality subconsciously. The system is targeted at handling various situations for five-year old kindergarteners by giving them caring words during their everyday lives. With the analysis of each case study, we provide a setting for a computational method to identify user behavioral patterns. We believe that the proposed link between the behavioral patterns and the mental state of a human user can be applied to improve not only user interactivity but also believability of the system.

Representing Emotions with Linguistic Acuity

Hye-Jin Min and Jong C. Park
Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Mexico City, Mexico, February 18-24, 2007.
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For a robot to make e๏ฌ€ective and friendly interaction with human users, it is important to keep track of emotional changes in utterance properly. Emotions have traditionally been characterized by intuitive but atomic categories or as points in evaluation-activity dimensions. However, this characterization falls short of capturing subtle emotional changes either in narration or in text, where the vast majority of information is presented with a host of linguistic constructions that convey emotional information. We propose a novel representation scheme for emotions, so that such important features as duration, target and intensity can also be treated as ๏ฌrst-class citizens and systematically accounted for. We argue that it is with this new mode of representation that the subtlety of the emotional ๏ฌ‚ow in utterance can be properly addressed. We use this representation to encode the emotional states and intentions of characters in the drama scripts for soap opera and describe how it is utilized in conjunction with parsing for lexicalized grammars.

Identifying Emotional Cues in Dialogue Sentences According to Targets

Hye-Jin Min and Jong C. Park
HCI Conference Korea, Phoenix Park, February, 2007.
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์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ๋Œ€ํ™” ๋˜๋Š” ์ปดํ“จํ„ฐ๋ฅผ ๋งค๊ฐœ๋กœ ์ด๋ฃจ์–ด์ง€๋Š” ๋Œ€ํ™”์—์„œ ์ž๊ธฐ๋…ธ์ถœ์€ ์„œ๋กœ์— ๋Œ€ํ•œ ๊ฐœ์ธ์ ์ธ ์ •๋ณด๋ฅผ ๊ณต์œ ํ•˜์—ฌ ์นœ๋ฐ€ํ•œ ๊ด€๊ณ„๋ฅผ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ๊ณผ์ •์ด๋‹ค. ์ž๊ธฐ๋…ธ์ถœ์—์„œ์˜ ๊ฐœ์ธ์ ์ธ ์ •๋ณด๋Š” ์ƒ๊ฐ ๋ฐ ๊ฒฝํ—˜์„ ๋น„๋กฏํ•˜์—ฌ ๊ฐ์ • ๋“ฑ์„ ์˜๋ฏธํ•˜๋Š”๋ฐ, ๊ฐ์ •์€ ํŠนํžˆ ๋Œ€ํ™” ๋ถ„์œ„๊ธฐ ํ˜•์„ฑ ๋ฐ ์›ํ™œํ•œ ๋Œ€ํ™” ์ง„ํ–‰์„ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์˜์‚ฌ์†Œํ†ต์ˆ˜๋‹จ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ๋Œ€ํ™” ์‹œ์˜ ๊ฐ์ •๋…ธ์ถœ์€ ๋Œ€ํ™” ์ƒ๋Œ€๋ฐฉ(๋…ธ์ถœ ๋Œ€์ƒ)๊ณผ ๊ฐ์ •ํ‘œํ˜„์˜ ๋Œ€์ƒ(ํ‘œํ˜„ ๋Œ€์ƒ)์— ๋”ฐ๋ผ ํ‘œํ˜„์˜ ์‹ค์ œ๊ฐ•๋„์™€ ๋…ธ์ถœ์˜ ์ •๋„๊ฐ€ ๋‹ฌ๋ผ์ง€๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธํ„ฐ๋„ท์„ ํ†ตํ•ด ๋Œ€ํ™”๋ฅผ ์ฃผ๊ณ  ๋ฐ›๊ฑฐ๋‚˜ ์ž๋ฃŒ๋ฅผ ์ „์†กํ•  ์ˆ˜ ์žˆ๋Š” ์ธ์Šคํ„ดํŠธ ๋ฉ”์‹ ์ €๋ฅผ ํ†ตํ•˜์—ฌ ์ด๋ฃจ์–ด์ง„ ๋Œ€ํ™”์—์„œ ๋…ธ์ถœ ๋Œ€์ƒ๊ณผ ํ‘œํ˜„ ๋Œ€์ƒ์„ ๊ณ ๋ คํ•˜์—ฌ ๋Œ€ํ™”์ฐธ์—ฌ์ž์˜ ๊ฐ์ •์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•œ ์‚ฌ์ „์กฐ์‚ฌ๋กœ ๋“œ๋ผ๋งˆ ์Šคํฌ๋ฆฝํŠธ ์ƒ์˜ ๋“ฑ์žฅ์ธ๋ฌผ๋“ค์˜ ๊ฐ์ •ํ‘œํ˜„ ํŒจํ„ด์„ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋…ธ์ถœ ๋Œ€์ƒ์ด ๊ฐ๊ฐ ๋‹ค๋ฅธ ๋Œ€ํ™”๋ฌธ์žฅ์—์„œ ํ†ต์‚ฌ ๋ฐ ์˜๋ฏธ ๋ถ„์„ ๊ณผ์ •์„ ๊ฑฐ์ณ ํ‘œํ˜„ ๋Œ€์ƒ์— ๋”ฐ๋ฅธ ๋Œ€ํ™”์ฐธ์—ฌ์ž์˜ ๊ฐ์ •์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๋Œ€ํ™”์ฐธ์—ฌ์ž๊ฐ€ ์ž์‹ ์˜ ๊ฐ์ •์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ๋Š” ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์ œ๊ณตํ•œ๋‹ค.

Searching Animation Models with a Lexical Ontology for Text Animation

Eunyoung Chang, Hee-Jin Lee, and Jong C. Park
HCI Conference Korea, Phoenix Park, February, 2007.

Customized Emotion Representation for Automatic Generation of Emotionally Appropriate Dialogs

Hye-Jin Min and Jong C. Park
the Korean Society for Emotion & Sensibility, KIST, May, 2006.
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๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ์šฉ์ž์—๊ฒŒ ์˜ํ™” ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๊ณ  ์˜ํ™”๋ฅผ ์ถ”์ฒœํ•ด ์ฃผ๋Š” ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉ์ž์™€ ์‹œ์Šคํ…œ ๊ฐ„์˜ ๋Œ€ํ™” ๋ง๋ญ‰์น˜๋ฅผ ๋ถ„์„ํ•˜์—ฌ ๋Œ€ํ™”๋ฌธ์— ๋‚˜ํƒ€๋‚˜๋Š” ๋ณดํŽธ์  ๋˜๋Š” ๊ฐœ๋ณ„์  ๊ฐ์ • ์ •๋ณด๋ฅผ ์‹๋ณ„ํ•˜๊ณ  ์ด๋“ค์„ ๊ธฐ์ˆ ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค. ๊ฐ์ •์„ ํ‘œํ˜„ํ•˜๋Š” ์–ธ์–ด ์ •๋ณด๋Š” ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ๋Œ€ํ™”๋ฌธ์œผ๋กœ๋ถ€ํ„ฐ ์ž๋™์œผ๋กœ ์ถ”์ถœ๋˜์–ด ๊ฐ์ •์ด ํฌํ•จ๋œ ๋Œ€ํ™”๋ฌธ ์‘๋‹ต ์ƒ์„ฑ์— ํ™œ์šฉ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ๋กœ ๋Œ€ํ™” ๋ง๋ญ‰์น˜ ๋ถ„์„์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ๊ธฐ์ˆ ๋ฐฉ๋ฒ•์˜ ์ ์ ˆ์„ฑ ๋ฐ ์œ ์šฉ์„ฑ์— ๋Œ€ํ•œ ํ‰๊ฐ€๋ฅผ ํ•˜๊ณ  ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค.

Personalized Background Music Recommendation System for User Generated Contents using Collective Intelligence

Doojin Park and Jong C. Park
the Korean Society for Emotion & Sensibility, KIST, May, 2006.
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์ตœ๊ทผ ์‹ธ์ด์›”๋“œ์™€ ๊ฐ™์€ ๋ธ”๋กœ๊ทธ ์„œ๋น„์Šค๋“ค์—์„œ ๋งŽ์€ ์‚ฌ์šฉ์ž๋“ค์€ ์ž์‹ ์˜ ๊ธ€์„ ๊ฒŒ์‹œํ•˜๋ฉด์„œ ์ด์— ๋งž๋Š” ๋ฐฐ๊ฒฝ์Œ ์•…์„ ํ•จ๊ป˜ ์˜ฌ๋ฆฌ๊ณ  ์žˆ๋‹ค. ์ด๋•Œ, ์‚ฌ์šฉ์ž๊ฐ€ ์ข‹์•„ํ•˜๋Š” ์Œ์•…์ด๋‚˜ ์‚ฌ์šฉ์ž๊ฐ€ ํŒ๋‹จํ•˜๊ธฐ์— ๊ธ€์˜ ๋ถ„์œ„๊ธฐ์— ๋งž๋Š” ์Œ ์•…์„ ์„ ์ •ํ•ด์„œ ์˜ฌ๋ฆฌ๊ฒŒ ๋˜๋‚˜ ์ ์ ˆํ•œ ์Œ์•…์„ ์„ ์ •ํ•˜๊ธฐ๋Š” ์‰ฝ์ง€ ์•Š๋‹ค. ํ•œํŽธ ๊ธฐ์กด ์Œ์•…์ถ”์ฒœ ์‹œ์Šคํ…œ์—์„œ๋Š” ํŠน ์ • ์Œ์•…์— ๋Œ€ํ•ด ์ „๋ฌธ๊ฐ€๊ฐ€ ์Œ์•…์ด๋ก ์— ๋”ฐ๋ผ ๋ถ„์„ํ•˜์—ฌ ๊ธฐ์ž…ํ•œ ๊ฐ์„ฑ์ •๋ณด๋ฅผ ์ด์šฉํ•˜๊ฑฐ๋‚˜ ์Œ์•…์˜ ํŒŒํ˜•์„ ๋ถ„์„ ํ•ด์„œ ์–ป์€ ๊ฐ์„ฑ์ •๋ณด๋ฅผ ์ด์šฉํ•˜๋‚˜ ์Œ์•…์˜ ํŠน์„ฑ์ƒ ์Œ์•…์—์„œ ๋Š๋ผ๋Š” ๊ฐ์„ฑ๋“ค์€ ๊ฐœ์ธ์ ์ธ ์„ฑํ–ฅ์— ๋”ฐ๋ผ ๋‹ค๋ฅด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ๋ธ”๋กœ๊ทธ์— ์˜ฌ๋ฆฌ๋Š” ๊ธ€์„ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ๋กœ ๋ถ„์„ํ•˜์—ฌ ๊ธ€์ด ๋‹ด๊ณ  ์žˆ๋Š” ๊ฐ์„ฑ์ •๋ณด ๋ฅผ ํฌํ•จํ•œ ์ƒํ™ฉ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ณ , ์ด๋Ÿฐ ์ •๋ณด์— ํ•ด๋‹นํ•˜๋Š” ๋ฐฐ๊ฒฝ์Œ์•…์„ ์‚ฌ์šฉ์ž ์ •๋ณด๋ฅผ ๊ฐ์•ˆํ•˜์—ฌ ์ž๋™์œผ๋กœ ์ถ”์ฒœํ•ด์ฃผ๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค.

u-SPACE: Ubiquitous Smart Parenting and Customized Education

Hye-Jin Min, Doojin Park, Eunyoung Chang, Ho-Joon Lee, and Jong C. Park
HCI Conference Korea, Phoenix Park, February, 2006.
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๋ถ€๋ชจ์˜ ์‚ฌํšŒ ํ™œ๋™ ์‹œ๊ฐ„์ด ๋Š˜์–ด๋‚จ์— ๋”ฐ๋ผ ์•„์ด๋“ค์ด ํ˜ผ์ž ์ง‘์—์„œ ๋ณด๋‚ด๋Š” ์‹œ๊ฐ„๋„ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์•„์ด๋“ค์˜ ์ž๋ฆฝ์‹ฌ์„ ํฌ๊ฒŒ ์ œํ•œํ•˜์ง€ ์•Š์œผ๋ฉด์„œ ๋…ธ์ถœ๋˜๊ธฐ ์‰ฌ์šด ์‹ค๋‚ด ์œ„ํ—˜์œผ๋กœ๋ถ€ํ„ฐ ์•„์ด๋“ค์„ ๋ณดํ˜ธํ•˜๊ณ  ์•„์ด์˜ ์‹ฌ๋ฆฌ, ๊ฐ์ •์  ์ƒํƒœ์— ๋”ฐ๋ผ ์ ์ ˆํ•œ ์ง€๋„๋ฅผ ํ•ด์ฃผ๋Š” ๋„์›€์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” RFID ๊ธฐ์ˆ ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์•„์ด๋“ค์„ ๋ฌผ๋ฆฌ์  ์œ„ํ—˜์œผ๋กœ๋ถ€ํ„ฐ ๋ณดํ˜ธํ•˜๊ณ  ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์•„์ด์˜ ์‹ฌ๋ฆฌ, ๊ฐ์ • ์ƒํƒœ์— ๋”ฐ๋ฅธ ์Œ์•…๊ณผ ์• ๋‹ˆ๋ฉ”์ด์…˜์˜ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์ฝ˜ํ…์ธ ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋˜ํ•œ ์ง€์†์ ์ธ ๊ด€์‹ฌ์ด ํ•„์š”ํ•œ ์ผ์ • ๊ด€๋ฆฌ, ์ผ์ƒ ์ƒํ™œ์—์„œ ๋„์›€์„ ์ฃผ๋Š” ์ „์ž์ œํ’ˆ ์‚ฌ์šฉ๋ฒ• ์•ˆ๋‚ด ๋“ฑ์˜ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์•„์ด ์Šค์Šค๋กœ ์ž์‹ ์˜ ์ผ์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์›€์„ ์ค€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐ€์ƒ์˜ ๊ฐ€์ •์„ ๋””์ž์ธํ•˜์—ฌ ์‹คํ˜„ ๊ฐ€๋Šฅํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ด์™€ ๊ฐ™์€ ์„œ๋น„์Šค๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค.

Customized Speech Synthesis for Children with Characteristic Behavioral Patterns

Ho-Joon Lee and Jong C. Park
HCI Conference Korea, Phoenix Park, February, 2006.
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์Œ์„ฑ์„ ํ†ตํ•œ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ์ •๋ณด ๊ตํ™˜ ๋ฐฉ๋ฒ•์€ ์ถ”๊ฐ€์ ์ธ ํ›ˆ๋ จ ๊ณผ์ •์ด๋‚˜ ์žฅ๋น„๊ฐ€ ํ•„์š”ํ•˜์ง€ ์•Š๊ณ  ๊ณต๊ฐ„ ์ œ์•ฝ์ด ๊ฑฐ์˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๋…ธ์•ฝ์ž ๋“ฑ ์‚ฌ์šฉ์ž์˜ ์—ฐ๋ น๋Œ€์— ๊ด€๊ณ„์—†์ด ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์Œ์„ฑ ์ •๋ณด๋Š” ์‹œ๊ฐ์ด๋‚˜ ์ด‰๊ฐ ๋“ฑ ๋‹ค๋ฅธ ์ •๋ณด ์ˆ˜ ๋‹จ๊ณผ์˜ ์ƒํ˜ธ ์ž‘์šฉ์œผ๋กœ ์ƒ์Šน ํšจ๊ณผ๋ฅผ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๊ณผ ๊ธฐ๊ณ„ ์‚ฌ์ด ์˜ ์ธํ„ฐํŽ˜์ด์Šค๋กœ ํ™œ์šฉ๋  ๊ฒฝ์šฐ ์ •๋ณด ์ „๋‹ฌ๋ ฅ์„ ๋†’์ด๋ฉด์„œ ์‚ฌ์šฉ์ž ์นœํ™”์ ์ธ ์„œ๋น„ ์Šค๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋™์ผํ•œ ์ƒํ™ฉ์—์„œ ๋™์ผํ•œ ์œ ํ˜•์˜ ์Œ์„ฑ ์ •๋ณด๊ฐ€ ์‚ฌ์šฉ์ž์—๊ฒŒ ์ง€์†์ ์œผ๋กœ ์ œ๊ณต๋  ๊ฒฝ์šฐ ํ‘œํ˜„์ƒ์˜ ๋‹จ์กฐ๋กœ์›€์œผ๋กœ ์ธํ•ด ์ •๋ณด ์ „๋‹ฌ ๋ ฅ์ด ๊ธ‰๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์ ๋„ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์Œ์„ฑ์„ ํ†ตํ•œ ์ •๋ณด ์ „๋‹ฌ ์˜ ๊ฒฝ์šฐ ๋™์ผ ์ƒํ™ฉ์ด๋ผ ํ•˜๋”๋ผ๋„ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ํŒจํ„ด, ์‹ฌ๋ฆฌ ์ƒํƒœ, ์ฃผ๋ณ€ ํ™˜๊ฒฝ ๋“ฑ์— ๋”ฐ๋ผ ์ฐจ๋ณ„ํ™”๋œ ๋ฌธ์žฅ ๊ตฌ์กฐ ๋ฐ ์–ดํœ˜์˜ ์„ ํƒ์œผ๋กœ ๊ธด์žฅ๊ฐ์„ ์œ ์ง€์‹œ์ผœ ์ค„ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 5 ์„ธ ์ „ํ›„์˜ ์–ด๋ฆฐ์ด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ทธ๋“ค์˜ ํ–‰๋™ ํŒจ ํ„ด ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ฐœ๋ณ„ํ™”๋œ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ฒฐ๊ณผ๋ฅผ ์ œ๊ณตํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์œ ์น˜์›์ด๋ผ๋Š” ๋ฌผ๋ฆฌ์  ๊ณต๊ฐ„์—์„œ ์–ด๋ฆฐ์ด๋“ค์˜ ์ฃผ๋œ ํ–‰๋™ ํŒจํ„ด์„ ๋ถ„์„ํ•˜๊ณ , ํ˜„์ง ์œ ์น˜์› ๊ต์‚ฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋™์ผํ•œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๋Š” ์กฐ๊ฑด์„ ํ†ต ํ•˜์—ฌ ์–ด๋ฆฐ์ด์˜ ํ–‰๋™ ํŒจํ„ด๊ณผ ์œ„์น˜ ์ •๋ณด, ์—ฐ๋ น ๋ฐ ์„ฑ๊ฒฉ์— ๋”ฐ๋ฅธ ๋ฐœํ™” ๋ฌธ์žฅ์˜ ๋ฌธ ์žฅ ๊ตฌ์กฐ์™€ ์–ดํœ˜์  ํŠน์„ฑ์„ ํŒŒ์•…ํ•œ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ, ๊ฐœ๋ณ„ํ™”๋œ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ฒฐ๊ณผ๋ฅผ ์œ„ํ•ด ์œ ์น˜์› ๊ณต๊ฐ„์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜๊ณ  RFID ๋ฅผ ์ด์šฉํ•˜์—ฌ ์–ด๋ฆฐ์ด์˜ ํ–‰๋™ ํŒจํ„ด ๋ฐ ์œ„์น˜ ์ •๋ณด๋ฅผ ํŒŒ์•…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋ถ„์„๋œ ๋ฐœํ™”๋ฌธ์˜ ๋ฌธ์žฅ ๊ตฌ ์กฐ์™€ ์–ดํœ˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜์—ฌ ์Œ์„ฑ์œผ๋กœ ํ•ฉ์„ฑ๋  ๋ฌธ์žฅ์˜ ๋ฌธ์žฅ ๊ตฌ์กฐ ๋ฐ ์–ดํœ˜๋ฅผ ์žฌ ๊ตฌ์„ฑํ•˜์—ฌ ์‚ฌ์šฉ์ž ๊ฐœ๋ณ„ํ™”๋œ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ฒฐ๊ณผ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์–ด๋ฆฐ์ด์˜ ํ–‰๋™ ํŒจํ„ด์ด ๋ฐœํ™”๋ฌธ์˜ ๋ฌธ์žฅ ๊ตฌ์กฐ ๋ฐ ์–ดํœ˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ ์‚ดํŽด๋ณด๊ณ  ์žฌ๊ตฌ์„ฑ๋œ ๊ฒฐ๊ณผ ๋ฐœํ™”๋ฌธ์„ ํ‰๊ฐ€ํ•œ๋‹ค.

Effective text visualization for biomedical information

Tak-eun Kim and Jong C. Park
HCI Conference Korea, Phoenix Park, February, 2007.
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์ƒ๋ฌผ ์˜๋ฃŒ ๋ถ„์•ผ์—์„œ ์ •๋ณด์˜ ์–‘์ด ์•„์ฃผ ๋น ๋ฅด๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ์ •๋ณด์—์„œ ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์—ฐ๊ตฌ๋“ค์ด ๋งŽ์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ๊ทธ๋ ‡์ง€๋งŒ ์ด๋ ‡๊ฒŒ ๋ฝ‘์•„์ง„ ์ •๋ณด์กฐ์ฐจ ๊ทธ ์–‘์ด ๋ฐฉ๋Œ€ํ•˜๊ณ , ๋˜ํ•œ ํ…์ŠคํŠธ๋กœ ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ง๊ด€์ ์œผ๋กœ ์ดํ•ดํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ์ •๋ณด๋“ค์„ ์ข€ ๋” ์ง๊ด€์ ์œผ๋กœ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ •๋ณด ์‹œ๊ฐํ™” ์‹œ์Šคํ…œ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ตœ๊ทผ ๋“ค์–ด ์ด๋Ÿฌํ•œ ์ •๋ณด ์‹œ๊ฐํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜์—ˆ์œผ๋‚˜ ์ด๋Ÿฌํ•œ ์‹œ๊ฐํ™” ์ •๋ณด์กฐ์ฐจ ๋„ˆ๋ฌด๋‚˜ ๋ฐฉ๋Œ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ์šฉ์ž๊ฐ€ ํ•„์š”๋กœ ํ•˜๋Š” ์ •๋ณด๋ฅผ ์—ฌ๊ณผํ•ด ์ฃผ๋Š” ๋ฐฉ๋ฒ•์ด ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์‹œ๊ฐํ™” ์‹œ์Šคํ…œ์—์„œ์˜ ์ง€์‹ ๋ฐœ๊ฒฌ์„ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒ๋ฌผ ์˜๋ฃŒ ์ •๋ณด์˜ ํ…์ŠคํŠธ ์‹œ๊ฐํ™”์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์ƒ๋ฌผ ์˜๋ฃŒ ์ •๋ณด์˜ ํšจ๊ณผ์ ์ธ ํ‘œํ˜„ ๋ฐฉ๋ฒ•๊ณผ ์ง€์‹ ๋ฐœ๊ฒฌ์„ ์œ„ํ•œ ์ง๊ด€์ ์ธ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค.

Semantic Representation for Temporal Adverbs and Temporal Morphemes

Eunyoung Chang and Jong C. Park
Proceedings of Annual Conference of the KSLI (Korea Society for Language and Information), pp. 193-207, Kangwon, Korea, 2006.
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์ƒํ™ฉ์€ ๋ฌธ์žฅ์—์„œ ์ฃผ๋กœ ์šฉ์–ธ์œผ๋กœ ๊ธฐ์ˆ ๋˜๋ฉฐ, ์ƒํ™ฉ์˜ ์‹œ๊ฐ„์  ์˜๋ฏธ๋Š” ์‹œ๊ฐ„์–ด์— ์˜ํ•ด ๋”ฐ๋กœ ํ‘œํ˜„๋œ๋‹ค. ์ด ์ค‘์—์„œ๋„ ์‹œ๊ฐ„ ๋ถ€์‚ฌ์™€ ์‹œ์ƒ ํ˜•ํƒœ์†Œ(์„ ์–ด๋ง ์–ด๋ฏธ)๊ฐ€ ์‹œ์ œ์™€ ์ƒ์— ๊ฒฐ์ •์ ์œผ๋กœ ๊ธฐ์—ฌํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ์—ฌ๋Ÿฌ ์„ฑ๋ถ„์ด ๋ฌธ์žฅ ๋‚ด์—์„œ ๋ณตํ•ฉ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ ์„ฑ๋ถ„์˜ ์˜๋ฏธ์™€ ๊ธฐ๋Šฅ์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง ์˜๊ฒฌ์ด ์ •๋ฆฌ๋˜์ง€ ์•Š์€ ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒํ™ฉ์˜ ์‹œ๊ฐ„์  ์†์„ฑ์„ ๋ถ„๋ฅ˜ํ•˜๊ณ , ์‹œ๊ฐ„ ๋ถ€์‚ฌ์™€ ์‹œ์ƒ ํ˜•ํƒœ์†Œ๊ฐ€ ๊ฐ ์†์„ฑ์— ๋ผ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์—ฌ ์–ดํœ˜ ๋‹จ์œ„์˜ ์˜๋ฏธ ํ‘œํ˜„ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๊ฐ„ ๋ถ€์‚ฌ๋Š” ์ƒํ™ฉ์‹œ์˜ ์œ„์น˜๋‚˜ ์ƒํ™ฉ์˜ ์‹œ๊ฐ„์  ์†์„ฑ์„ ์ˆ˜์‹ํ•˜๊ณ , ์‹œ์ƒ ํ˜•ํƒœ์†Œ๋Š” ๋ฐœํ™”์‹œ์™€ ์ƒํ™ฉ์‹œ์˜ ๊ด€๊ณ„ ๋˜๋Š” ํ™”์ž์˜ ์ƒํ™ฉ์— ๋Œ€ํ•œ ํƒœ๋„๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ ์ ˆํ•œ ์–ดํœ˜ ๋ฒ”์ฃผ๋ฅผ ์ œ์‹œํ•˜๊ณ , ์ด๋“ค์˜ ๊ฒฐํ•ฉ์— ์˜ํ•˜์—ฌ ์ตœ์ข… ์˜๋ฏธ๊ฐ€ ๋„์ถœ๋˜๋Š” ๊ณผ์ •์„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ํ†ตํ•œ ์ฒ˜๋ฆฌ ๊ณผ์ •์œผ๋กœ ๋ณด์ธ๋‹ค.

CCG-based RNA Secondary Structure Prediction for Structural Homology Analysis

Hee-Jin Lee and Jong C. Park
6th International Conference on Genome Informatics (GIW), Yokohama, Japan, December, 2005.
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Various systems have been proposed to predict secondary structures of RNAs using their sequence information. Among them, Uemura et al. [2] described a system that recognizes some typical RNA secondary structures such as hairpin loops and pseudoknots with Tree Adjoining Grammar. However, their work captures only known sub-structures, and not those unknown sub-structures that might also exist. Ternary pseudoknot, composed of three pairs of cross-serially arranged reverse-complementary sequences, may be one such example. Figure 1 illustrates an example ternary pseudoknot. We describe a version of Combinatory Categorial Grammars (CCGs) for an RNA secondary prediction system to discover such unknown sub-structures. The parser for the proposed CCG takes an RNA sequence and produces the semantics string that contains structural information of the sequence.

From Text to Sign Language: Exploiting the Spatial and Motioning Dimension

Jiwon Choi, Hee-Jin Lee, and Jong C. Park
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation (PACLIC 19), pp. 61-69, Taipei, Taiwan, December, 2005.
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In this paper, we address the problem of automatically converting information in the Korean language to one in a sign language as used in Korea. First, we discuss the differences between sign language and natural language, and in particular between the sign language in Korea and the Korean language. Then, we focus on issues that are relevant to the process of converting expressions in Korean into their counterparts in the sign language, including: 1) making explicit elided subjects of expressions in Korean, 2) omitting some expressions in Korean, and 3) reordering some expressions. We argue that it is important to utilize the spatial and motioning dimensionality of a sign language in order to minimize information loss and distortion. We also argue that the right decision to omit, or to merge some expressions in Korean plays a key role in exploiting this dimensionality. Finally, we present a system that converts sentences in Korean into corresponding animations in the sign language as proof of evidence for our claim.

Dynamic Informative Link Annotation for Biological Text over Heterogeneous Databases

Hodong Lee and Jong C. Park
16th International Conference on Genome Informatics (GIW), Yokohama, Japan, December, 2005.
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Linking from a textual object to the biological databases is actively performed for an e๏ฌƒcient data access and information enrichment [2]. This task targets at a link for particular types of term, such as names, keywords and symbols, that correspond to each data entry. However, such one-to-one matching links are still insu๏ฌƒcient to make a full use of biological data in numerous databases. The previous researches have reported further problems: (1) The conceptual term referring to multiple data objects cannot be represented as a one-to-one link [1]; (2) the complex term often corresponds to the data objects from multiple databases [6]; (3) the link must be consistent with the data objects that can be changed or removed from a database [4]; and (4) the term is ambiguous due to the semantic and syntactic heterogeneity, which requires not only the structural and operational pieces of database information but also the biological pieces of knowledge about the term semantics [4, 5]. We address all the problems above with a dynamic link annotation system that annotates links by formulating the database statement in a formal query language. We are currently developing the system for 13 molecular biology databases mediated by SRS and Entrez: GO, GOA, UniProt, InterPro, EMBL, and Enzyme in SRS; Gene, Protein, Nucleotide, PubMed, OMIM, HomoloGene, and Taxonomy in Entrez.

Vowel Sound Disambiguation for Intelligible Korean Speech Synthesis

Ho-Joon Lee and Jong C. Park
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation (PACLIC 19), pp. 131-142, Taipei, Taiwan, December, 2005.
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For speech synthesis systems that transform text materials into voice data, correctness and naturalness are the crucial measures of performance, the latter gaining more emphasis recently. In order to make synthesized voices natural, we must take into account pronunciation-related linguistic phenomena such as homograph, among others. The syntax certainly provides an important clue to disambiguating such homographs, but the relatively free word order in the Korean language makes it hard to utilize such information. In this paper, we describe a computational generation of contextually appropriate vowel lengths for the words in Korean by utilizing a higher level of linguistic information in a Combinatory Categorial Grammar framework. We consider parts-of- speech information, the possibility of conjunction with a suffix, case information, unconjugated adjectives, numerals, numerical adjectives with related nouns, and the relationship between a noun and its predicate as syntactic and semantic clues for vowel sound disambiguation. The results are expressed in Speech Synthesis Markup Language (SSML) for a target system neutral application. The proposed system with correctly predicted vowel sound can be used not only as an educational tool, but also as a plug-in for enhancing the intelligibility of a general purpose Text-to-Speech (TTS) system.

Text Animation with Music

Doojin Park and Jong C. Park
Proceedings of the 32th Korea Information Science Society (KISS), Vol. 32, No. 2, pp. 526-528, Seoul, November, 2005.
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์Œ์•…์€ ์Šคํ† ๋ฆฌํ…”๋ง์—์„œ ์ด์•ผ๊ธฐ์˜ ๋ถ„์œ„๊ธฐ์™€ ํ๋ฆ„์„ ์ „๋‹ฌํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์ตœ๊ทผ ์ปดํ“จํ„ฐ ์• ๋‹ˆ๋ฉ”์ด์…˜์— ์ž๋™์œผ๋กœ ์•Œ๋งž์€ ์Œ์•…์„ ์‚ฝ์ž…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ์ง€๋งŒ ์ด์•ผ๊ธฐ๊ฐ€ ์žˆ๋Š” ์• ๋‹ˆ๋ฉ”์ด์…˜๋ณด๋‹ค๋Š” ์ฃผ๋กœ ์˜์ƒ๋ฌผ์˜ ๋™๊ธฐํ™”๋ฅผ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด์—ˆ๋‹ค. ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ”์ด์…˜์€ ๋™ํ™”๋ฅผ ์ž๋™์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ๋งŒ๋“ค์–ด์ฃผ๋Š” ์—ฐ๊ตฌ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋™ํ™”์˜ ์ด์•ผ๊ธฐ ๊ตฌ์กฐ์— ๊ทผ๊ฑฐํ•˜์—ฌ ๊ฐ ์žฅ๋ฉด์˜ ๋ถ„์œ„๊ธฐ์— ๋งž๋Š” ์Œ์•… ์ž์งˆ์„ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ๊ณผ์ •์„ ๋ณด์ด๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ”์ด์…˜์— ์Œ์•…์ด ์‚ฝ์ž…๋  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค.

Prediction of RNA Secondary Structures in a Combinatory Categorial Grammar Framework

Hee-Jin Lee and Jong C. Park
Proceedings of the First International Symposium on Languages in Biology and Medicine (LBM), pp. 59-62, KAIST, Daejeon, Korea, November, 2005.
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In this paper, we define a Combinatory Categorial Grammar (CCG) to model and predict RNA secondary structures. The proposed CCG can be used to capture various RNA secondary structures, including stem-loop and pseudoknot structures. We also argue that the CCG can be used to predict possibly unknown RNA secondary structures, for example an undiscovered structure 'ternary-pseudoknots'.

Automated Linking of Conceptual and Complex Terms into Data Objects in Biological Databases

Hodong Lee and Jong C. Park
Proceedings of the First International Symposium on Languages in Biology and Medicine (LBM), pp. 51-54, Creative Learning Building, KAIST, Daejeon, Korea, November, 2005.
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The purpose of a textual link is to provide a one-to-one connection between a term and a related data object. However, this link is insufficient to deal with the conceptual and complex terms that are often used to refer to multiple data objects from heterogeneous databases. In this paper, we present a method that can dynamically create a link to a biological term by automatically constructing a database query for a search into the corresponding data object(s). This method can help the user to quickly build a hypothesis based on data drawn from text, as well as to understand the text by providing an access to relevant information for its biological terms.

Generation of Coherent Gene Summary with Concept-Linking Sentences

Chan-Goo Kang and Jong C. Park
Proceedings of the First International Symposium on Languages in Biology and Medicine (LBM), pp. 41-45, Creative Learning Building, KAIST, Daejeon, Korea, November, 2005.
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Typical approaches to automatic summarization make efforts to generate a coherent document by arranging the order of sentences according to certain criteria such as the publication date of the text in which the expression appears. However, when describing a gene, there is no obvious order whatsoever among the facts to be presented. In this work, while generating a summary about a gene, we actually create the order from the unordered set of facts, by introducing new sentences that make associations among the main concepts of those facts.

CCG-based RNA Secondary Structure Prediction

Hee-Jin Lee and Jong C. Park
The First International Symposium on Languages in Biology and Medicine (LBM), Daejeon, Korea, November, 2005.
Show abstract
In this paper, we define a Combinatory Categorial Grammar (CCG) to model and predict RNA secondary structures. The proposed CCG can be used to capture various RNA secondary structures, including stem-loop and psudoknot structures. We also argue that the CCG can be used to predict possibly unknown RNA secondary structures, for example an undiscovered structure 'ternary-pseudoknots'.

Dynamic and Informative Linking from Biological Text into Heterogeneous Databases

Hodong Lee and Jong C. Park
The First International Symposium on Languages in Biology and Medicine (LBM), Daejeon, Korea, November, 2005.
Show abstract
Linking from a textual object to the biological databases is actively performed for an e๏ฌƒcient data access and information enrichment [2]. This task targets at a link for particular types of term, such as names, keywords and symbols, that correspond to each data entry. However, such one-to-one matching links are still insu๏ฌƒcient to make a full use of biological data in numerous databases. The previous researches have reported further problems: (1) The conceptual term referring to multiple data objects cannot be represented as a one-to-one link [1]; (2) the complex term often corresponds to the data objects from multiple databases [6]; (3) the link must be consistent with the data objects that can be changed or removed from a database [4]; and (4) the term is ambiguous due to the semantic and syntactic heterogeneity, which requires not only the structural and operational pieces of database information but also the biological pieces of knowledge about the term semantics [4, 5]. We address all the problems above with a dynamic link annotation system that annotates links by formulating the database statement in a formal query language. We are currently developing the system for 13 molecular biology databases mediated by SRS and Entrez: GO, GOA, UniProt, InterPro, EMBL, and Enzyme in SRS; Gene, Protein, Nucleotide, PubMed, OMIM, HomoloGene, and Taxonomy in Entrez.

Intonation Synthesis using Emotional Information from Spoken Fairy Tale

Ho-Joon Lee and Jong C. Park
Proceedings of the 17th Korean Association of Speech Science (KASS), pp. 88-97, Seoul, November 26, 2005.
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์ •๋ณด ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ ๋ถ€๊ฐ๋˜๋ฉด์„œ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ์ˆ ์˜ ํ™œ์šฉ์ด ์ ์  ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋Š” ์ถ”์„ธ์ด๋‹ค. ์ž์—ฐ์Šค๋Ÿฌ์šด ์Œ์„ฑ ํ•ฉ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ๋ฐœํ™” ์ƒํ™ฉ์— ์ ํ•ฉํ•œ ์–ต์–‘ ์ •๋ณด๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๊ณ , ํŠนํžˆ ๊ฐ์ •์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ž์—ฐ์Šค๋Ÿฌ์šด ์Œ์„ฑ ํ•ฉ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ์–ต์–‘ ์ •๋ณด ์ค‘์—์„œ๋„ ์Œ์˜ ๋†’๋‚ฎ์ด๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๊ฐ์ • ์ •๋ณด๋ฅผ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ์ˆ ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ฐ์ • ์ •๋ณด๊ฐ€ ์ž˜ ํ‘œํ˜„๋˜์–ด ์žˆ๋Š” ์Œ์„ฑ ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„์ด ์„ ํ–‰ ๋˜์–ด์•ผ ํ•˜๊ณ , ์ด์™€ ๊ด€๋ จํ•œ ์ž๋ฃŒ๋กœ์„œ ๋™ํ™” ๊ตฌ์—ฐ ์Œ์„ฑ ๋ฐ์ดํ„ฐ๋Š” ์•„์ด๋“ค์—๊ฒŒ ๋ณด๋‹ค ์‚ฌ์‹ค๊ฐ ์žˆ๋Š” ๋‚ด์šฉ ์ „๋‹ฌ์„ ์œ„ํ•ด ๊ฐ์ • ์ •๋ณด๊ฐ€ ํ’๋ถ€ํ•˜๊ฒŒ ํ‘œํ˜„๋˜์–ด์žˆ๋Š” ํŠน์ง•์ด ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋™ํ™” ๊ตฌ ์—ฐ ์ „๋ฌธ๊ฐ€์— ์˜ํ•ด ๋…น์Œ๋œ ์ „๋ž˜ ์ธํ˜•๊ทน์„ ๋ถ„์„ํ•˜์—ฌ ๊ฐ์ • ์ƒํƒœ์— ๋”ฐ๋ฅธ ๋ฐœํ™”๋ฌธ์˜ ์Œ์šด ํŠน์„ฑ์„ ์‚ดํŽด๋ณด๊ณ , ์ด๋Ÿฌํ•œ ๊ฐ์ • ์ •๋ณด์™€ ๋ฌธ์žฅ์˜ ํ†ต์‚ฌ, ์˜๋ฏธ ๊ตฌ์กฐ ๋“ฑ ์–ธ์–ดํ•™์ ์ธ ์ •๋ณด์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ฐ” ํƒ•์œผ๋กœ ๊ฐ์ • ์ •๋ณด๋ฅผ ์Œ์„ฑ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์— ์ œ๊ณตํ•˜์—ฌ ์ ์ ˆํžˆ ๊ตฌ์‚ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ๋…ผ์˜ํ•œ๋‹ค.

Modeling Causality in Biological Pathways for Logical Identification of Drug Targets

Il Park and Jong C. Park
Proceedings of the 2005 International Joint Conference of InCoB, AASBi and KSBI (Bioinfo 2005), pp. 373-378, Busan, Korea, September, 2005.

Lexical Disambiguation for Intonation Synthesis: A CCG Approach

Ho-Joon Lee and Jong C. Park
Korean Society for Language and Information (KSLI), June 17-18, 2005.
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IT์˜ ๊ธ‰๊ฒฉํ•œ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์ •๋ณด ์ „๋‹ฌ ๋ฐฉ๋ฒ•์ด ์ง€์†์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋ฉด์„œ ์šฐ๋ฆฌ๋ง์˜ ์ •ํ™•ํ•œ ๋ฐœ์Œ์— ๋Œ€ํ•œ ์ธ์‹์ด ์ ์  ์•ฝํ™”๋˜๊ณ  ์žˆ๋Š” ์ถ”์„ธ์ด๋‹ค. ํŠนํžˆ ์žฅ๋‹จ์Œ์˜ ๋ฐœ์Œ์€ ๋ฐœํ™”์— ๋Œ€ํ•œ ์ „๋ฌธ์ธ๋“ค๋„ ์ •ํ™•ํ•˜๊ฒŒ ๊ตฌ๋ถ„ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋Š” ์‹ฌ๊ฐํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด ๋ช…์‚ฌ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์žฅ๋‹จ์Œ ํ™” ํ˜„์ƒ์„ ์ฃผ๋ณ€ ์–ดํœ˜์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‚ดํŽด๋ณด๊ณ  ๋™์Œ์ด์˜์–ด ์ค‘ ๋‹ค๋ฅด๊ฒŒ ๋ฐœ์Œ๋˜๋Š” ๋ช…์‚ฌ์˜ ์žฅ๋‹จ์Œ ๊ตฌ๋ถ„์„ ๋ช…์‚ฌ์™€ ๋ช…์‚ฌ์˜ ์ˆ˜์‹์–ด, ๋ช…์‚ฌ์˜ ์„œ์ˆ ์–ด์™€์˜ ๊ด€๊ณ„๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋…ผ์˜ํ•œ๋‹ค. ๋ถ„์„๋œ ๊ฒฐ๊ณผ๋Š” ๊ฒฐํ•ฉ๋ฒ” ์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ํ‘œํ˜„ํ•˜๊ณ  ์–ดํœ˜์  ์ค‘์˜์„ฑ์ด ํ•ด์†Œ๋œ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ณผ์ •์„ ํ‘œ์ค€ํ™”๋œ SSML (Speech Synthesis Markup Language)์œผ๋กœ ๊ธฐ์ˆ ํ•œ๋‹ค.

Induced Extension of Gene Ontology from Biomedical Resources with Flexible Identification of Candidate Terms

Jin-Bok Lee, Jung-jae Kim, and Jong C. Park
The First International Symposium on Semantic Mining in Biomedicine (SMBM), page 13, Cambridge, UK, April, 2005.
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Motivation: We present a novel method to predict more detailed terms than those in the present Gene Ontology (GO). We apply this method to semantic tagging for natural language expressions that denote potential GO terms even when there is no direct mapping of such expressions into GO terms. The terms that are newly identi๏ฌed in this process can be used to extend GO by utilizing semantic relations such as hyponyms or synonyms. Finally, we suggest how to find a suitable direction for the possible extension of an ever-growing ontology such as GO.
Results: We provide an automatically extended GO, and tools for its manipulation and validation.
Availability: http://www.biopathway.org
Contact: park@nlp.kaist.ac.kr

Automatic Generation of Multimedia Animation from Play Scripts

Doojin Park and Jong C. Park
HCI Conference Korea, 2005.
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ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ”์ด์…˜์€ ์ž์—ฐ์–ธ์–ด๋ฌธ์žฅ์œผ๋กœ๋ถ€ํ„ฐ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜ ๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์ž‘๊ฐ€์˜ ์˜๋„๋Œ€๋กœ ์‹คํ˜„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์บ๋ฆญํ„ฐ์˜ ํ–‰๋™๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ถ€๊ฐ€์ ์ธ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ํšจ๊ณผ๊ฐ€ ํ•„์ˆ˜์ ์œผ๋กœ ์š”๊ตฌ๋œ ๋‹ค. ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ •๋ณด๋Š” ์ผ๋ฐ˜์ ์ธ ํ…์ŠคํŠธ์—์„œ ์ถฉ๋ถ„ํžˆ ์ œ๊ณต๋˜์ง€ ์•Š์ง€๋งŒ ์—ฐ๊ทน ๊ณต์—ฐ์„ ์œ„ํ•œ ๋Œ€๋ณธ์—๋Š” ๋‹ค์–‘ํ•œ ๋ถ€๊ฐ€ ์ •๋ณด๋“ค์ด ์–ด๋Š ์ •๋„ ์ •ํ˜•์  ์œผ๋กœ ์ œ์‹œ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์—ฐ๊ทน ๋Œ€๋ณธ์˜ ๋Œ€์‚ฌ, ์ง€๋ฌธ, ํ•ด์„ค์„ ์ž๋™์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ์บ๋ฆญํ„ฐ ์˜ ํ–‰๋™๊ณผ ์Œํ–ฅ์ด ํ†ตํ•ฉ๋œ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์„ ๋ณด์ธ๋‹ค. ์Œํ–ฅ์€ ๊ทน์  ํšจ๊ณผ๋ฅผ ์œ„ํ•œ ๊ธฐ๋ณธ์ ์ธ ์žฅ์น˜๋กœ, ์บ๋ฆญํ„ฐ์˜ ํ–‰๋™๊ณผ ํšจ๊ณผ์ ์œผ๋กœ ํ†ต ํ•ฉ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ฐ๊ทน ๋Œ€๋ณธ์—์„œ ํ‘œํ˜„๋œ ์Œํ–ฅํšจ๊ณผ๋ฅผ ์ง์ ‘ ์ถ”์ถœํ•˜๊ฑฐ๋‚˜ ์ƒ์‹์ • ๋ณด๋ฅผ ์ด์šฉํ•œ ์ถ”๋ก ์œผ๋กœ ์ ํ•ฉํ•œ ์Œํ–ฅ์„ ์ž…์ฒด์ ์ด๊ณ  ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋งž๊ฒŒ ํ‘œํ˜„ํ•ด ์ฃผ์–ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณผ์ •์„ ์œ„ํ•ด ์—ฐ๊ทน ๋Œ€๋ณธ์˜ ์ž์—ฐ์–ธ์–ด ํ‘œํ˜„์„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ์บ๋ฆญํ„ฐ์˜ ํ–‰๋™๊ณผ ์Œํ–ฅํšจ๊ณผ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์ถ”์ถœํ•˜๊ณ  ์ด์— ๋”ฐ๋ฅด๋Š” ์บ๋ฆญํ„ฐ์˜ ํ–‰๋™๊ณผ ์Œํ–ฅํšจ๊ณผ๋ฅผ 3D ๋ชจ๋ธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ์Œํ–ฅ ๋ฐ์ดํ„ฐ๋ฒ  ์ด์Šค๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์• ๋‹ˆ๋ฉ”์ด์…˜์œผ๋กœ ์ƒ์„ฑํ•œ๋‹ค.

Emotion Prediction from Natural Language Documents with Emotion Network

Hye-Jin Min and Jong C. Park
Proceedings of HLT, pp. 191-199, Ulsan, October, 2004.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ…์ŠคํŠธ์— ๋‚˜ํƒ€๋‚œ ๊ฐ์ •์ƒํƒœ๋ฅผ ์ธ์ง€ํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋Ÿฌํ•œ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ํ˜„์žฌ๋ฌธ์žฅ์—์„œ ๋‚˜ํƒ€๋‚œ ๊ฐ์ • ๋ฐ ์ดํ›„์— ๋‚˜ํƒ€๋‚˜๊ฒŒ ๋  ๊ฐ์ •์ƒํƒœ๋“ค์„ ์˜ˆ์ธกํ•˜๋Š” ์‹œ์Šคํ…œ์— ๋Œ€ํ•˜์—ฌ ๋‹ค๋ฃฌ๋‹ค. ์‚ฌ์šฉ์ž์˜ ๊ฐ์ •์„ ์ธ์ง€ํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ฉ”์‹œ์ง€, ํ–‰๋™ ๋“ฑ์„ ํ†ตํ•ด ์ธ๊ฐ„๊ณผ ์ƒํ˜ธ์ž‘์šฉ ํ•  ์ˆ˜ ์žˆ๋Š” ์ปดํ“จํ„ฐ์‹œ์Šคํ…œ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ˜„์žฌ์˜ ๊ฐ์ •์ƒํƒœ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‚ฌ์šฉ์ž ๊ฐœ๊ฐœ์ธ์˜ ์ •๋ณด ๋ฐ ์‹œ์Šคํ…œ๊ณผ ์ƒํ˜ธ์ž‘์šฉํ•˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์˜ ์ •๋ณด ๋“ฑ์„ ํ†ตํ•ด ์ดํ›„์—์‚ฌ์šฉ์ž๊ฐ€ ๋Š๋‚„ ์ˆ˜ ์žˆ๋Š” ๊ฐ์ •์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ์ •๋ชจ๋ธ์ด ์š”๊ตฌ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŒŒ์•…๋œ ์ด์ „์˜ ๊ฐ์ •์ƒํƒœ ๋ฐ ์‹ค์ œ ๊ฐ์ •๊ณผ ํ‘œํ˜„๋œ ๊ฐ์ •๊ฐ„์˜ ๊ด€๊ณ„, ๊ทธ๋ฆฌ๊ณ  ๊ฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ์ฃผ๋ณ€๋Œ€์ƒ์˜ ํŠน์ง• ๋ฐ ๊ฐ์ •๊ฒฝํ—˜์ž์˜ ๋ชฉํ‘œ์™€ ํ–‰๋™์ด ๋ฐ˜์˜๋œ ์ƒํƒœ-์ „์ดํ˜•ํƒœ์˜ ๊ฐ์ •๋ชจ๋ธ์ธ ๊ฐ์ •๋ง(Emotion Network)์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ์ •๋ง์€ ๊ฐ ๊ฐ์ •์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ƒํƒœ(state)์™€ ์—ฐ๊ฒฐ๋œ ์ƒํƒœ๋“ค ๊ฐ„์˜ ์ „์ด(transition), ๊ทธ๋ฆฌ๊ณ  ์ „์ด๊ฐ€ ๋ฐœ์ƒํ•˜๊ธฐ ์œ„ํ•œ ์กฐ๊ฑด(condition)์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ…์ŠคํŠธ ํ˜•ํƒœ์˜ ์ƒ๋‹ด์˜ˆ์‹œ์— ๊ฐ์ •๋ง์„ ํ™œ์šฉํ•˜์—ฌ ๋ฌธํ—Œ์˜ ๊ฐ์ •์–ดํœ˜์— ์˜ํ•ด ์ง์ ‘์ ์œผ๋กœ ํ‘œ์ถœ๋˜์ง€ ์•Š๋Š” ๊ฐ์ •์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.

Identification and Recovery of Elided Information for Text Animation

Eunyoung Chang and Jong C. Park
Proceedings of HLT, pp. 94-102, Ulsan, October, 2004.
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์Œ์„ฑ์ธ์‹๊ธฐ์ˆ ์„ ์‹ค์ œ ์ƒํ™œ์— ์ ์šฉํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ๋Œ€ํ‘œ์ ์ธ ๋ฌธ์ œ๋กœ, ์ธ์‹๊ธฐ์˜ ๋‚ฎ์€ ์ธ์‹๋ฅ ๋กœ ์ธํ•œ ์˜ค๋™์ž‘์„ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š”, ํ…”๋ ˆ๋ฑ…ํ‚น ๋„๋ฉ”์ธ์—์„œ์˜ HTK(Hidden Markov Model Toolkit) ์—ฐ์† ์Œ์„ฑ ์ธ์‹ ์‹œ์Šคํ…œ๊ณผ, ์ตœ๋Œ€ ์—”ํŠธ๋กœํ”ผ ๊ธฐ๋ฒ•์— ๊ธฐ๋ฐ˜ํ•œ ์‚ฌ์šฉ์ž ๋ฐœํ™”์—์„œ์˜ ํ•ต์‹ฌ์ด ๋˜๋Š” ๋‹จ์–ด(์ฃผ๋กœ ๊ณ ์œ  ๋ช…์‚ฌ๋“ค)๋“ค์— ๋Œ€ํ•œ ์ธ์‹ ์‹ ๋ขฐ๋„์˜ ์ธก์ • ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์Œํ–ฅํŠน์ง•๊ณผ ์–ธ์–ดํŠน์ง•๋“ค์„ ๋ชจ๋‘ ๊ณ ๋ คํ•˜์—ฌ ์ธ์‹ ์‹ ๋ขฐ๋„๋ฅผ ๊ตฌํ•˜์˜€์œผ๋ฉฐ ์ธ์‹๋œ ๋‹จ์–ด๋“ค์— ๋Œ€ํ•ด ์˜ค์ธ์‹ ๋˜์—ˆ์Œ์„ ์•ฝ 86%์˜ ์ •ํ™•๋„๋กœ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธ ํ•˜์˜€๋‹ค. ๋ณธ ์ธ์‹์‹ ๋ขฐ๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฐจํ›„์— ์Œ์„ฑ์ธ์‹์˜ ํ™•์ธ๋Œ€ํ™”(Clarification Dialog)๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ํ™œ์šฉํ•˜๊ณ ์ž ํ•œ๋‹ค.

Constructing VoiceXML documents with Contextually Appropriate Intonation from Natural Language Dialogues in a Combinatory Categorial Grammar framework

Lee Hwa Jin, Ho-Joon Lee, and Jong C. Park
Proceedings of the 5th China-Korea Joint Symposium on Oriental Language Processing and Pattern Recognition, pp. 2-9, Qingao, P.R.China, February 25-27, 2004.
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Various natural language processing techniques have been utilized to enhance the performance of the Text-to-Speech (TTS) systems to date. Correctness and naturalness are among the working measures for the performance of these systems, where the usual proposals to satisfy the second measure have employed statistic prediction methods to ๏ฌnd appropriate intonation for a given sequence of words in a sentence. However, these proposals tend to assign the same intonation to the same word sequence in a sentence, whereas people may associate quite different kinds of intonation with the same word sequence in a sentence depending upon the context in which the sentence is expressed. In this paper, we use a combinatory categorial grammar approach to synthesizing contextually appropriate intonation for dialogues in Korean, taking into account the distinguishing characteristics as identi๏ฌed from the speech corpus. The intonation-annotated dialogues are then translated into corresponding VoiceXML documents, which work as direct inputs to a TTS system for the generation of actual speech data.

Anaphora Resolution in Text Animation

Kyung Wha Hong and Jong C. Park
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pp. 347-352, Innsbruck, Austria, February, 2004.
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For effective text animation from natural language stories, the source sentences in natural language should be processed not only individually but also as a coherent story as a whole. In particular, it is important that anaphoric expressions are interpreted adequately, since they provide crucial clues for the overall behaviors of story line characters. In text understanding, the task of anaphora resolution has been primarily on nominal expressions. In text animation, however, there are many other important candidates for anaphoric expressions, including those for actions and events, in addition to objects. In this paper, we provide an analysis of sample fairy tales, and present a classification for the types of anaphoric expressions for text animation. We also describe an implemented text animation system with anaphora resolution.

Case Study: Visualization and Analysis of Mitogen-Activated Protein Kinase Pathways in the Literature

Changsu Lee, Jinah Park, and Jong C. Park
Conference on Visualization and Data Analysis (VDA), pp. 275-285, San Jose, USA, Janurary, 2004.
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Data sets of up to 3000 journal abstracts from MEDLINE literature on the keyword combination 'MAPK pathway' and 'human' are visualized and analyzed for mitogen-activated protein kinase (MAPK) pathways. We have tightly coupled exploratory visualization with information extraction for interactive navigation through scattered information sources, in search of useful facts on MAPK by frequency-based filtering and amplification Unlike direct database visualization that operates on curated data sets, literature visualization has the advantages of manipulating data sets of a massive scale with a lot less manpower and effectively responding to the fast cycles of the developments in the field.

BioAR: Anaphora Resolution for Relating Protein Names to Proteome Database Entries

Jung-jae Kim and Jong C. Park
ACL Workshop on Reference Resolution and its Applications, pp. 79-86, Barcelona, Spain, 2004.
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The need for associating, or grounding, protein names in the literature with the entries of proteome databases such as Swiss-Prot is well-recognized. The protein names in the biomedical literature show a high degree of morpholog- ical and syntactic variations, and various anaphoric expressions including null anaphors. We present a biomedical anaphora resolution system, BioAR, in order to address the variations of protein names and to further associate them with Swiss-Prot entries as the actual entities in the world. The system shows the performance of 59.5%โœ‚75.0% precision and 40.7%โœ‚56.3% recall, depending on the specific types of anaphoric expressions. We apply BioAR to the protein names in the biological interactions as extracted by our biomedical information extraction system, or BioIE, in order to construct protein pathways automatically.

Annotation of Gene Products in the Literature with Gene Ontology Terms using Syntactic Dependencies

Jung-jae Kim and Jong C. Park.
Proceedings of the 1st International Joint Conferrence on Natural Language Processing (IJCNLP), pp. 528-534, Hainan, P.R.China, 2004.
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We present a method for automatically annotating gene products in the literature with the terms of Gene Ontology (GO), which provides a dynamic but controlled vocabulary. Although GO is well-organized with such lexical relations as synonymy, โ€˜is-aโ€™, and โ€˜part-ofโ€™ relations among its terms, GO terms show quite a high degree of morphological and syntactic variations in the literature. As opposed to the previous approaches that considered only restricted kinds of term variations, our method uncovers the syntactic dependencies between gene product names and ontological terms as well in order to deal with real-world syntactic variations, based on the observation that the component words in an ontological term usually appear in a sentence with established patterns of syntactic dependencies.

Automatic Camera Control for Automated Digital Cinematography from Text

Semin Jang and Jong C. Park
Proceedings of the 31th KISS Spring Conference, Vol. 31, No. 1(B), pp. 904-906, KAIST, Korea, 2004.
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์˜ํ™”๋ฅผ ์ œ์ž‘ํ•˜๋Š” ๊ณผ์ •์— ํ•„์ˆ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ๋Œ€๋ณธ(่‡บๆœฌ)์—๋Š” ํ•„์š”ํ•œ ๋ถ€๋ถ„๋งˆ๋‹ค ์˜์ƒ๊ธฐ๋ฒ•์ด ๋ช…์‹œ๋˜์–ด ์žˆ์–ด์„œ ์‹ค์ œ ์žฅ๋ฉด์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ณผ์ •์— ์›์ž‘์ž๊ฐ€ ์˜๋„ํ•˜๋Š” ์ƒํ™ฉ์„ ๋น„๊ต์  ์ •ํ™•ํ•˜๊ฒŒ ์žฌํ˜„ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด์— ๋น„ํ•˜์—ฌ ๊ตํ†ต์‚ฌ๊ณ  ์‚ฌ๊ฑด๋ณด๊ณ ์„œ๋‚˜ ๋™ํ™” ๋“ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋””์ง€ํ„ธ ์˜์ƒ์„ ์ž๋™์œผ๋กœ ์ œ์ž‘ํ•˜๋ ค๋Š” ๊ฒฝ์šฐ ์ด๋Ÿฌํ•œ ์˜์ƒ๊ธฐ๋ฒ•์ด ๋ช…์‹œ๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ž์—ฐ์–ธ์–ด๋กœ ๊ธฐ์ˆ ๋œ ์ž๋ฃŒ๋กœ ๋ถ€ํ„ฐ ๋””์ง€ํ„ธ ์˜์ƒ์„ ์ž๋™์œผ๋กœ ์ œ์ž‘ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ž‘๊ฐ€์˜ ์˜๋„๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ์ ์ ˆํ•œ ์˜์ƒ๊ธฐ๋ฒ•์„ ์ถ”์ถœ ํ•˜๋Š” ๋ฐฉ์•ˆ์ด ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ๋Š” ๋™ํ™”๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์• ๋‹ˆ๋ฉ”์ด์…˜ ์ž๋™ ์ƒ์„ฑ์„ ์œ„ํ•ด์„œ ์‹œ๊ฐ„ ๊ด€๋ฆฌ, ์ฐธ์กฐ ํ•ด๊ฒฐ, ์œ„์น˜ ์„ค์ •, ์„ธ๋ถ€ ๋ช…๋ น ๊ฒฐ์ • ๋ฐ ๋‹ค์ˆ˜ ์บ๋ฆญํ„ฐ ์ œ์–ด ๋“ฑ์˜ ์š”์†Œ ๊ธฐ์ˆ ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์ด๊ณ  ํŠนํžˆ ์‹œ๊ฐ„ ๊ด€๋ฆฌ ์ค‘์—์„œ ์ ์ ˆํ•œ ์žฅ๋ฉด์ „ํ™˜์ด ํ•„์š”ํ•œ ๊ฒฝ์šฐ๋ฅผ ์ž๋™์œผ๋กœ ํŒŒ์•…ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋™ํ™” ๋ฌธ์žฅ์— ๋‚˜ํƒ€๋‚˜๋Š” ์ž‘๊ฐ€์˜ ์˜๋„๋ฅผ ๋ถ„์„ํ•˜๊ณ , ์ด์— ๋ถ€ํ•ฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ์นด๋ฉ”๋ผ ์šด์šฉ๊ธฐ๋ฒ•์„ ์ž๋™์œผ๋กœ ํŒŒ์•…ํ•˜์—ฌ ์ ์šฉํ•œ ๋””์ง€ํ„ธ ์˜์ƒ ์ œ์ž‘ ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ  ๊ตฌํ˜„ํ•œ ์‹œ์Šคํ…œ์„ ๋ณด์ธ๋‹ค.

Automatic Generation of Multimedia Therapeutic Contents with Combinatory Categorial Grammar

Hye-Jin Min and Jong C. Park
HCI/CG/VR/UI/DESIGN, Phoenix Park, 2004.
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์ธํ„ฐ๋„ท์˜ ๋ฐœ๋‹ฌ๋กœ ๋Œ€์•ˆ์ ์ธ ์‹ฌ๋ฆฌ์น˜๋ฃŒ ๋ฐฉ๋ฒ•์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ๋‹ด์น˜๋ฃŒ, ์Œ์•…์น˜ ๋ฃŒ ๋ฐ ๋ฏธ์ˆ ์น˜๋ฃŒ๊ฐ€ ๊ฐœ์ธ์˜ ๊ณ ๋ฏผ์„ ์ƒ๋‹ดํ•ด ์ฃผ๋Š” ์ธํ„ฐ๋„ท ์‚ฌ์ดํŠธ์—์„œ ํ™œ๋ฐœํžˆ ์ œ ๊ณต๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‚ด๋‹ด์ž์˜ ๊ณ ๋ฏผ์ด ๋‹ด๊ธด ๊ธ€์„ ์ž๋™์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ๋‚ด๋‹ด์ž์˜ ๊ฐ์ • ์ƒํƒœ์™€ ๊ณ ๋ฏผ์˜ ์›์ธ ์ •๋ณด๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ๊ธ€, ๊ทธ๋ฆผ, ์Œ์•… ๋“ฑ์ด ํ†ต ํ•ฉ๋œ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์น˜๋ฃŒ ์ •๋ณด๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์„ ๋ณด์ธ๋‹ค. ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์น˜๋ฃŒ ์ • ๋ณด๋Š” ํ•ด๋‹น ๊ฐ์ •์˜ ํ•ด์†Œ์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ํ…์ŠคํŠธ, ์ด๋ฏธ์ง€ ๋ฐ ์Œ์•…ํŒŒ์ผ์ด ์‹ฌ๋ฆฌ์ ์ธ ์น˜๋ฃŒ์˜ ๋ชฉ์ ์œผ๋กœ ๊ฒ€์ƒ‰์–ด์™€ ํ•จ๊ป˜ ๊ตฌ์กฐํ™”๋˜์–ด ์žˆ๋Š” ์ •๋ณด๋ฅผ ์ง€์นญํ•œ๋‹ค. ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์น˜๋ฃŒ ์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•œ ๊ฒ€์ƒ‰์–ด๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋Š” ๋ฌธ์žฅ์—์„œ ๊ณ ๋ฏผ์— ๊ด€๋ จ๋˜๋Š” ๋‚ด๋‹ด์ž์˜ ๊ฐ์ •ํ‘œํ˜„ ๋ฐฉ์‹ ๋ฐ ์˜๋ฏธ ๊ด€๊ณ„, ๊ทธ๋ฆฌ๊ณ  ํ•ด๋‹น ๊ฐ์ •์˜ ๊ฒฝ๊ณผ ์‹œ๊ฐ„ ์ •๋ณด ๋“ฑ์„ ์ ์ ˆํžˆ ๋ถ„์„ํ•ด๋‚ด์•ผ ํ•˜๋ฏ€๋กœ, ํ‚ค์›Œ๋“œ์— ๋”ฐ๋ผ ์ด์— ๋งž๋Š” ๊ฐ์ •์„ ๋Œ€์‘์‹œํ‚ค๊ฑฐ๋‚˜ ์ƒ์‹์„ ์ด์šฉํ•˜์—ฌ ์ถ”๋ก ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๊ฐ์ • ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค๋ฃจ์ง€ ์•Š์•˜๋˜ ์ถ”๊ฐ€์ ์ธ ์–ธ์–ด์  ํŠน ์„ฑ๋“ค์ด ๋ณด๋‹ค ์‹ฌ๋„์žˆ๊ฒŒ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋ฅผ ์œ„ํ•˜์—ฌ ๋‚ดํฌ๋ฌธ ์ด๋‚˜ ์ ‘์†๋ฌธ๊ณผ ๊ฐ™์€ ํ•˜์œ„๋ฌธ์˜ ์ฃผ์–ด์™€ ์ƒ์œ„๋ฌธ์˜ ์ฃผ์–ด๊ฐ€ ์„œ๋กœ ๊ฐ€์ง€๋Š” ๊ด€๊ณ„๋ฅผ ์ž๋™์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ , ๊ฐ ๋™์‚ฌ๊ฐ€ ์˜๋ฏธ์ ์œผ๋กœ ์š”๊ตฌํ•˜๋Š” ๋ฌธ์žฅ์„ฑ๋ถ„์˜ ์„ฑ๊ฒฉ์— ๋”ฐ๋ผ ๊ฐ์ •์˜ ๊ฒฝํ—˜์ฃผ ๋ฐ ํ‘œํ˜„์˜ ๋Œ€์ƒ์„ ํ™•์ธํ•˜๋ฉฐ ์‹œ๊ฐ„๋ถ€์‚ฌ๋กœ ๊ฐ์ •๋ณ€ํ™”์ƒํƒœ๋ฅผ ํŒŒ์•… ํ•˜๋Š” ๋“ฑ์˜ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ํ†ตํ•˜์—ฌ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ์ด๋“ค ๋ฌธ์žฅ์— ๋‚˜ํƒ€๋‚˜ ์žˆ๋Š” ์‹ฌ๋ฆฌ์ƒํƒœ์— ๋Œ€์‘ํ•˜๋Š” ์น˜๋ฃŒ ์ •๋ณด๋ฅผ ๊ตฌ์กฐํ™”๋œ ๋ฐ์ดํ„ฐ๋ฒ ์ด ์Šค๋กœ๋ถ€ํ„ฐ ๊ฒ€์ƒ‰ํ•˜์—ฌ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ์น˜๋ฃŒ ์ •๋ณด๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์„ ๋ณด์ธ๋‹ค.

Data-oriented Customized Visual Navigation

Changsu Lee, Jinah Park, and Jong C. Park
HCI/CG/VR/UI/DESIGN, Phoenix Park, 2004.
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์ €์žฅ ๋งค์ฒด์˜ ๋ฐœ๋‹ฌ ๋ฐ ์ •๋ณด ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์ธํ•ด์„œ ๋น ๋ฅด๊ฒŒ ๋Š˜์–ด๋‚˜๋Š” ๊ฐ€์šฉ ํ•œ ์ •๋ณด์˜ ๋ฐฉ๋Œ€ํ•œ ์–‘์€ ์‚ฌ์šฉ์ž์˜ ์ •๋ณด์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์–ด๋ ต๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ •๋ณด์˜ ์›์ฒœ์œผ๋กœ๋ถ€ํ„ฐ ์ •๋ณด์˜ ์—ฌ๊ณผ, ์ •๋ณด์˜ ํ‘œํ˜„์œผ๋กœ ์ด์–ด์ง€๋Š” ์ผ๋ จ์˜ ์ •๋ณด ํ™œ์šฉ ๊ณผ ์ •์—์„œ, ์‚ฌ์šฉ์ž ๊ฐœ๋ณ„ํ™”์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ์ •๋ณด์˜ ์—ฌ๊ณผ ์ชฝ์— ์„œ๋งŒ ์ด๋ฃจ์–ด์ ธ ์™”๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌ์šฉ์ž์™€ ๊ฐ€๊น๊ฒŒ ์ƒํ˜ธ ์ž‘์šฉ์„ ํ•˜๋Š” ์ •๋ณด์˜ ํ‘œํ˜„ ๋ถ€๋ถ„์—์„œ ์‚ฌ์šฉ์ž ๊ฐœ๋ณ„ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•ด์ง€๋ฉด, ์‚ฌ์šฉ์ž๋Š” ์ž์‹ ์˜ ๋ชฉ์ ์— ๋ถ€ํ•ฉํ•˜๋Š” ์ • ๋ณด๋ฅผ ์–ป๋Š” ๊ณผ์ •์„ ๋”์šฑ ์„ธ๋ฐ€ํ•˜๊ฒŒ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ์šฉ์ž ๊ฐœ ๋ณ„ํ™” ๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ ์ ๊ทน์  ์—ญํ• ์˜ ์‹œ๊ฐํ™” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž ๊ฐœ๋ณ„ํ™” ๊ธฐ๋Šฅ์€ ๋ฐ์ดํ„ฐ์˜ ํŠน์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ๋ถ„๋ฅ˜๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋Š” ์ƒ๋ฌผํ•™์„ ์ ์šฉ ๋„๋ฉ”์ธ์œผ๋กœ ํ•˜์—ฌ, ๋ถ„์ž๊ฐ„ ์ƒํ˜ธ ์ž‘์šฉ ๋ฐ์ดํ„ฐ์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ, ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ์‚ฌ์šฉ์ž๋ณ„๋กœ ๊ฐœ๋ณ„ํ™”๋œ ๋ถ„ ์ž๊ฐ„ ์ƒํ˜ธ ์ž‘์šฉ ์ง€๋„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค.

Natural Language Response Generation from Relational Database Query Result

Ji-yong Jung and Jong C. Park
HCI/CG/VR/UI/DESIGN, Phoenix Park, 2004.
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์ž์—ฐ์–ธ์–ด ์งˆ์˜/์‘๋‹ต ์ธํ„ฐํŽ˜์ด์Šค๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ํŠน๋ณ„ํ•œ ์ง€์‹์ด ์—†์–ด๋„ ์‹œ์Šค ํ…œ์— ์‰ฝ๊ฒŒ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ, ์ •๋ณด์˜ ์ œ๊ณต์„ ์‰ฝ๊ณ  ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ํ•œ๋‹ค. ๊ทธ ๋Ÿฌ๋‚˜ ์ด์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋Š” ๋Œ€๋ถ€๋ถ„์ด ์ž์—ฐ์–ธ์–ด๋ฅผ SQL๊ณผ ๊ฐ™์€ ๋ฐ์ดํ„ฐ๋ฒ ์ด ์Šค ์ ‘๊ทผ์„ ์œ„ํ•œ ํ˜•์‹์–ธ์–ด๋กœ ๋ฐ”๊พธ๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๊ณ , ์งˆ์˜๋กœ๋ถ€ํ„ฐ ์–ป์–ด ์ง„ ๊ฒฐ๊ณผ๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ํ‘œํ˜„ํ•˜๋Š” ์‘๋‹ต ์ƒ์„ฑ์— ์žˆ์–ด์„œ๋Š” ์•„์ง ๋งŒ์กฑ์Šค๋Ÿฌ์šด ๊ฒฐ๊ณผ๋ฅผ ๋งŒ๋“ค์–ด๋‚ด์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ์ž์—ฐ์–ธ์–ด ์‘๋‹ต ์ƒ์„ฑ์„ ์œ„ํ•ด์„œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์•Œ๊ณ  ์žˆ ๋Š” ์ •๋ณด, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋‚ด์žฅ ์ •๋ณด, ๊ทธ๋ฆฌ๊ณ  ์‚ฌ์šฉ์ž๊ฐ€ ์งˆ์˜๋ฅผ ํ•จ์œผ๋กœ์จ ์–ป๊ณ ์ž ํ•˜๋Š” ์ •๋ณด๊ฐ€ ๋ณตํ•ฉ์ ์œผ๋กœ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ ์‚ฌ์šฉ์ž๊ฐ€ ๊ธฐ๋Œ€ํ•˜๋Š” ํ˜•ํƒœ์˜ ์‘๋‹ต์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์›ํ•˜๋Š” ์‘๋‹ตํ˜•ํƒœ๋ฅผ ์‚ฌ์ „์— ๋ชจ๋ธ๋งํ•˜๊ณ  ๊ฐ€์žฅ ์„ ํ˜ธ๋˜๋Š” ์‘๋‹ตํ˜•ํƒœ๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ์šฉ์ž์˜ ์งˆ์˜๋กœ๋ถ€ ํ„ฐ ์–ป์–ด์ง„ ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ์— ๋Œ€ํ•ด ์งˆ์˜์˜ ์˜๋„์— ๋งž๊ฒŒ ๊ฐœ๋ณ„ ํ™”๋œ ์‘๋‹ต์„ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์„ ๋‹ค๋ฃฌ๋‹ค. ์ ์ ˆํ•œ ์‘๋‹ต ์ƒ์„ฑ์„ ์œ„ํ•ด์„œ ์—ฌํ–‰์ƒํ’ˆ ์ •๋ณด์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž์˜ ์งˆ์˜/์‘๋‹ต ์ฝ”ํผ์Šค๋ฅผ ์ •๋ณด์˜ ๋‚ด์šฉ ๋ฐ ๋ถ„๋Ÿ‰ ์ธก๋ฉด์—์„œ ๋ถ„ ์„ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ด๊ณ , ์ด์— ๋”ฐ๋ผ ๋‚ด์šฉ๊ณ„ํš, ๋ฌธ์žฅ ํ˜•ํƒœ ๊ตฌ์„ฑ, ์–ดํœ˜ ํ‘œํ˜„์˜ ์„ธ ๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋Š” ๋ฌธ์žฅ ์ƒ์„ฑ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค.

Contextual Disambiguation of Adverbial Scopes in Korean for Text Animation

Eunyoung Chang, Kyung Wha Hong, and Jong C. Park
HCI/CG/VR/UI/DESIGN, Phoenix Park, 2004.
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์ž์—ฐ ์–ธ์–ด ๋ฌธ์žฅ์œผ๋กœ ๊ตฌ์„ฑ๋œ ํ…์ŠคํŠธ๋ฅผ ์• ๋‹ˆ๋ฉ”์ด์…˜์œผ๋กœ ์ž๋™ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ์„œ๋Š” ๋ฌธ์žฅ์˜ ํ†ต์‚ฌ ์ •๋ณด, ์˜๋ฏธ ์ •๋ณด, ๋‹ดํ™” ์ •๋ณด๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ์ผ๋ จ์˜ ์• ๋‹ˆ๋ฉ”์ด ์…˜ ๋ช…๋ น๋“ค์„ ๋„์ถœํ•ด ๋‚ด์•ผ ํ•œ๋‹ค. ๋ถ€์‚ฌ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์žฅ๋“ค์—์„œ ํ•ด๋‹น ์• ๋‹ˆ๋ฉ”์ด์…˜ ๋ช…๋ น์˜ ์†์„ฑ ๋ณ€ํ™” ์ •๋„๋ฅผ ๊ฒฐ์ •ํ•˜๋ฉฐ ๋ถ€์‚ฌ์˜ ๋‹ค์–‘ํ•œ ์ˆ˜์‹ ๋Œ€์ƒ๊ณผ ์˜๋ฏธ์˜ ์ •ํ™• ํ•œ ํ•ด์„์€ ํ…์ŠคํŠธ์˜ ์˜๋„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ถ€์‚ฌ์˜ ์ˆ˜์‹ ๋Œ€์ƒ ๋ฒ”์œ„๊ฐ€ ๋งค์šฐ ๋„“๊ณ  ๊ทธ ์˜๋ฏธ๋„ ๋‹ค์–‘ํ•˜์—ฌ, ๋‚ดํฌ์ ˆ์ด๋‚˜ ๋ณ‘๋ ฌ๊ตฌ์กฐ๋ฅผ ํฌํ•จํ•˜๋Š” ๋ณต์žกํ•œ ๋ฌธ์žฅ์—์„œ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹จ๋ฌธ์—์„œ๋„ ๋ถ€์‚ฌ์˜ ๊ธฐ๋Šฅ์„ ์ •ํ™•ํžˆ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์šฉ์ดํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ •ํ™•ํ•œ ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ” ์ด์…˜์„ ์œ„ํ•œ ๋ถ€์‚ฌ์˜ ๋ถ„์„๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ๊ทธ ์ฒ˜๋ฆฌ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค. ํ˜„์žฌ ์ด๋ฃจ ์–ด์ ธ ์žˆ๋Š” ํ•œ๊ตญ์–ด ๋ถ€์‚ฌ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ํ•™์Šต์œผ๋กœ ๋ถ€์‚ฌ์™€ ํ”ผ์ˆ˜ ์‹์–ด์™€์˜ ํ˜ธ์‘์„ฑ์„ ํ™œ์šฉํ•˜์—ฌ ๊ตฌ์กฐ์˜ ์• ๋งค์„ฑ์„ ์ฒ˜๋ฆฌํ•˜๊ณ  ์žˆ์„ ๋ฟ ์•„๋‹ˆ๋ผ, ๋ถ€ ์‚ฌ์˜ ์œ„์น˜ ์ œ์•ฝ ์ •๋ณด ์ค‘ ๊ทนํžˆ ์ผ๋ถ€๋งŒ์„ ์ด๋Ÿฌํ•œ ํ˜ธ์‘ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์ œ์•ฝ ์ •๋ณด ๋กœ ํ™œ์šฉํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ •๋ณด์— ๋ฌธ๋งฅ ์ •๋ณด๋ฅผ ๊ฐ™์ด ๊ณ ๋ คํ•˜์—ฌ ๊ตฌ์กฐ์  ์• ๋งค์„ฑ์„ ํ•ด๊ฒฐํ•˜๊ณ  ๋ณด๋‹ค ์ •ํ™•ํ•œ ์˜๋ฏธ๋ฅผ ๋„์ถœํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์— ์„œ๋Š” ๋ถ€์‚ฌ์˜ ํ†ต์‚ฌ์ , ์˜๋ฏธ์  ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๊ณ , ์ด๋ฅผ ํ™•์žฅํ•˜์—ฌ ํŒŒ์ƒ๋ถ€์‚ฌ, ๋ถ€์‚ฌ๊ตฌ, ๋ถ€์‚ฌ์ ˆ ๋“ฑ์˜ ๋ณต์žกํ•œ ๋ถ€์‚ฌ์–ด ๊ตฌ ๋ฌธ์— ๋Œ€ํ•ด์„œ๋„ ๋ฌธ๋ฒ•์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ ‡๊ฒŒ ์ œ์‹œ๋œ ๋ฐฉ์•ˆ์„ ๊ตฌํ˜„ํ•œ ํ…์ŠคํŠธ ์• ๋‹ˆ๋ฉ”์ด์…˜ ์‹œ์Šคํ…œ์„ ํ†ตํ•˜์—ฌ ์• ๋‹ˆ๋ฉ”์ด์…˜ ์ƒ์„ฑ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•œ๋‹ค.

Information Visualization in 3-Dimensional Space for Text Data Mining

Jinah Park, Changsu Lee, and Jong C. Park
International Women's Conference on BIEN-Technology, Daejeon, Korea, November, 2003.

Analysis and Computational Processing of Sentences in Korean for automatic sign language Generation

Jiwon Choi and Jong C. Park
Proceedings of the National Conference on Korean Language Processing, pp. 219-226, October, 2003.
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ํ•œ๊ตญ ์ˆ˜ํ™”๋Š” ํ•œ๊ตญ์–ด์— ๋Œ€ํ•œ ๊ธฐ๋ณธ์ ์ธ ์œ ์‚ฌ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ์ง€๋งŒ, ๊ต์ฐฉ์–ด์ด์ž ์ฒญ๊ฐ-์Œ์„ฑ ์ฒด ๊ณ„ ์–ธ์–ด์ธ ํ•œ๊ตญ์–ด์™€๋Š” ๋‹ฌ๋ฆฌ ๊ณ ๋ฆฝ์–ด์ด์ž ์‹œ๊ฐ-์šด๋™ ์ฒด๊ณ„ ์–ธ์–ด๋กœ์„œ์˜ ํŠน์„ฑ์„ ๋™์‹œ์— ๋‚˜ํƒ€๋‚ด ๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ํ…์ŠคํŠธ ํ˜•ํƒœ์˜ ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜ํ™”๋ฅผ ์ž๋™ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ•œ ๊ตญ์–ด๋ฅผ ์œ„ํ•ด ๋ฏธ๋ฆฌ ์ •์˜๋œ ๋ฌธ๋ฒ•์— ์ˆ˜ํ™” ํ‘œํ˜„์„ ๋ฌด๋ฆฌํ•˜๊ฒŒ ์—ฐ๊ณ„ ์‹œํ‚ค๋ ค๊ณ  ํ•˜๊ธฐ ๋ณด๋‹ค, ์ˆ˜ํ™” ๊ณ  ์œ ์˜ ์˜๋ฏธ ์ „๋‹ฌ ์ฒด๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ณ  ํ™œ์šฉํ•˜์—ฌ์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ˆ˜ํ™” ํ‘œํ˜„์ƒ ์˜ ์–ธ์–ดํ•™์  ํŠน์ง•์„ ์žฌํ˜„ยท์ƒ๋žตยท๋ณ€ํ˜•ยท์ด๋™์˜ ๋„ค ๊ฐ€์ง€๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋ถ„์„ํ•˜๊ณ  ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•œ ์ด ๊ฐ™์€ ํ˜„์ƒ์˜ ์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ• ๋ฐ ๊ตฌํ˜„ ๋ฐฉ์•ˆ์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค.

Towards Automatic Sign Language Generation with Combinatory Categorial Grammar

Jiwon Choi and Jong C. Park
HCI Conference, pp. 481-486, Phoenix Park, Korea, February, 2003.
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์ˆ˜ํ™”๋Š” ์ฒญ๊ฐ ์žฅ์• ์ธ์˜ ์˜์‚ฌ์†Œํ†ต์„ ์œ„ํ•œ ์‹œ๊ฐ์  ์–ธ์–ด๋ผ๋Š” ํŠน์ง•์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด ๊ตฌ์–ด ๋ณ‘์šฉ์„ ์ „์ œ๋กœ ํ•˜๋Š” ๋‹ค๋ฅธ ์–ธ์–ด์—์„œ๋Š” ์ฐพ์•„ ๋ณด๊ธฐ ์–ด๋ ค์šด ๋…ํŠนํ•œ ๋ฌธ๋ฒ• ๊ตฌ ์กฐ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜ํ™”๋ฅผ ์ž๋™์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๋ ค๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์–ด๋ฅผ ์œ„ํ•˜์—ฌ ๋ฏธ๋ฆฌ ์ •์˜๋œ ๋ฌธ๋ฒ•์— ์ˆ˜ํ™” ํ‘œํ˜„์„ ์—ฐ๊ณ„ ์‹œํ‚ค๋ ค๋Š” ๋…ธ๋ ฅ์ด ๋ฌด ๋ฆฌํ•˜๊ฒŒ ์„ ํ–‰๋˜์–ด ์ˆ˜ํ™” ๊ณ ์œ ์˜ ์˜๋ฏธ ์ „๋‹ฌ ์ฒด๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ํ™œ์šฉํ•˜๋Š”๋ฐ ๋งŽ์€ ๋ฌธ์ œ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ˆ˜ํ™”์—์„œ๋Š” ์ˆ˜๋™, ์ˆ˜ํ˜• ๋“ฑ์˜ ์ˆ˜ํ™”์†Œ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋™์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๋Š” ๊ธฐ์ œ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋„์น˜๋ฌธ์—์„œ์˜ ์ฃผ์–ด์™€ ๋ชฉ์ ์–ด ๊ด€๊ณ„, ์‚ฌ ๋™๊ณผ ํ”ผ๋™๋ฌธ์—์„œ ์ฃผ์ฒด์™€ ๊ฐ์ฒด ๊ด€๊ณ„ ๋“ฑ์„ ์• ๋งค์„ฑ ์—†์ด ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๊ณ , ์ง์ „ ์— ์ง€์ •๋œ ๊ณต๊ฐ„ ์ •๋ณด๋ฅผ ์ผ์ข…์˜ ์„ ํ–‰์‚ฌ์™€ ๊ฐ™์ด ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์ค‘๋ณต๋œ ํ‘œํ˜„์„ ํ”ผํ•˜์—ฌ ํšจ์œจ์ ์ธ ์ •๋ณด ์ „๋‹ฌ์„ ๊พ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด์™€ ๊ฐ™์€ ์ž์—ฐ ์–ธ์–ด ํ‘œํ˜„์„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜๋Š” ๊ณผ์ •์„ ํ†ตํ•˜์—ฌ ์ด๋“ค ํ‘œํ˜„์— ๋Œ€ ์‘ํ•˜๋Š” ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ๋™๋ฐ˜ํ•œ ์ˆ˜ํ™” ํ‘œํ˜„์œผ๋กœ ์ž๋™ ๋ฒˆ์—ญํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ณผ์ •์— ํ•„์ˆ˜์ ์œผ๋กœ ํ•„์š”ํ•œ ์š”์†Œ๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ด๊ณ  ์ˆ˜ํ™”์—์„œ ๋‚˜ํƒ€ ๋‚˜๋Š” ๋…ํŠนํ•œ ์–ธ์–ด ํ‘œํ˜„ ๊ธฐ๋ฒ•์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์—ฌ ๋ณด๋‹ค ์ž์—ฐ์Šค๋Ÿฌ์šด ์ˆ˜ํ™” ํ‘œํ˜„ ์„ ์ƒ์„ฑํ•˜๋Š” ๋ฐฉ์•ˆ์„ ๊ตฌํ˜„๊ณผ ํ•จ๊ป˜ ์ œ์‹œํ•œ๋‹ค.

Anaphora Resolution and Multi-Character Control for Automatic Generation of Multimedia Fairy Tales

Kyung Wha Hong and Jong C. Park
HCI Conference, pp. 487-492, Phoenix Park, Korea, February, 2003.
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ํ•œ๊ตญ์–ด์™€ ๊ฐ™์€ ์ž์—ฐ์–ธ์–ด๋กœ ์ž‘์„ฑ๋œ ๋ฌธ์žฅ์˜ ์—ฐ์†์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋ฌธ์„œ ํ˜•ํƒœ์˜ ๋™ ํ™”๋ฅผ ์ž…๋ ฅ์œผ๋กœ ๋ฐ›์•„ ๋™ํ™”์˜ ๋‚ด์šฉ์„ ์ ์ ˆํžˆ ๋ฐ˜์˜ํ•œ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ํฌํ•จํ•˜๋Š” ๋ฉ€ ํ‹ฐ ๋™ํ™”๋ฅผ ์ž๋™ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ•ด๋‹น ๋ฌธ์„œ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ์ข… ์ฐธ์กฐํ˜„์ƒ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ํ•ด์„์ด ํ•„์ˆ˜์ ์œผ๋กœ ์š”๊ตฌ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์œ„ํ•œ ์ฐธ ์กฐํ˜„์ƒ ํ•ด์„์€ ๋ฌธ์„œ์˜ ์ดํ•ด๋ฅผ ๋•๊ธฐ ์œ„ํ•˜์—ฌ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๋ถ„์•ผ์—์„œ ํ†ต์ƒ์ ์œผ ๋กœ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋Š” ์ฐธ์กฐํ˜„์ƒ ํ•ด์„์—์„œ๋ณด๋‹ค ์œ ํ˜•์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ๋ณด์ธ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฉ€ํ‹ฐ ๋™ํ™”๋ฅผ ์ž๋™ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์— ๋ฌธ์žฅ์˜ ์ฐธ์กฐํ˜„์ƒ๊ณผ ํ•จ๊ป˜ ๋‹ค์ˆ˜ ์บ๋ฆญํ„ฐ์˜ ์›€์ง์ž„์„ ์ ์ ˆํžˆ ๊ณ ๋ คํ•˜์—ฌ 3 ์ฐจ์› ๊ฐ€์ƒ ๊ณต๊ฐ„์„ ์ œ์–ดํ•˜๋Š” ๋ช…๋ น ์„ ์ƒ์„ฑํ•˜๋Š” ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ๊ตฌํ˜„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค. ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ์œ„ํ•œ ์ฐธ์กฐํ˜„ ์ƒ ํ•ด์„์€ ์ฐธ์กฐํ‘œํ˜„์˜ ์ ์ ˆํ•œ ์„ ํ–‰์‚ฌ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์„ ๊ทธ ๋ชฉ์ ์œผ๋กœ ํ•˜๊ณ  ์žˆ ๋Š”๋ฐ ์บ๋ฆญํ„ฐ์˜ ๋ช…์นญ, ๋™์ž‘, ์„ฑ์งˆ, ์‚ฌ๊ฑด, ์‹œ๊ฐ„ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์žฅ๋ฉด ์ •๋ณด๋“ค์— ๋Œ€ ํ•œ ๊ณ ๋ ค๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋‹ค์ˆ˜ ์บ๋ฆญํ„ฐ๋ฅผ ๋ฌธ๋งฅ์— ๋งž๊ฒŒ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ ์ ˆํ•œ ์ฐธ์กฐํ•ด๊ฒฐ๊ณผ ํ•จ๊ป˜ ๋‹ค์–‘ํ•œ ์ง€์‹์„ ํ™œ์šฉํ•˜์—ฌ ์บ๋ฆญํ„ฐ๋“ค์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์›€์ง์ž„์„ ์ œ๊ณตํ•˜๋Š” ๊ธฐ๋ฒ•์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋™ํ™”๋ฅผ ๋ถ„์„ํ•œ ๋’ค ์ด์— ํ•ด๋‹นํ•˜๋Š” Genesis 3D ๊ฒŒ์ž„์—”์ง„ ์ œ์–ด ์Šคํฌ๋ฆฝํŠธ๋ฅผ ์ž๋™ ์ƒ์„ฑํ•˜ ๋Š” ์‹œ์Šคํ…œ์„ ๋ณด์ธ๋‹ค.

Mediatory Visualization for Structured Data and Textual Information

Changsu Lee, Jinah Park, and Jong C. Park
The 3rd IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2003), pp. 926-932, Benalmadena, Spain, 2003.
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When we visualize structured data for knowledge discovery, it is important that the users have an easy access to the source textual information, especially when the map ping from the textual information to structured data is not perfect. In this paper, we present a new method for mediatory visualization for structured data and corresponding textual information to address this problem. The two dimensional space for visualizing structured data, such as the protein-protein interaction information collected from biomedical literature by information extraction, is linked perpendicularly to, but conceptually separated from, the pairwise one dimensional space for visualizing corresponding source textual data. The users can concentrate on the information in one space but explore the information in the other space as easily as one may manipulate objects in a three dimensional space. We show that the one dimensional color-banded rods give visual clues and insights to the nature of the underlying English sentence structures, which in turn give rise to useful feedback to the interaction information in the other two dimensional space, and vice versa.

Logical Representation of Ontological Terminologies in Biomedical Domain

Jung-jae Kim, Jin-Bok Lee, Hye-Jin Min, Ji-yong Jung, and Jong C. Park
Proceedings of the 2nd Annual Conference of The Korean Society for Bioinformatics (KSBI 2003), pp. 79-85, Daejeon, Korea, 2003.
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๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€๋Ÿ‰์˜ ์ƒ๋ฌผ์˜๋ฃŒ๋ถ„์•ผ ๋ฌธ์„œ์—์„œ ๋‹จ๋ฐฑ์งˆ ์ด๋ฆ„์„ ์ž๋™์œผ๋กœ ์ธ์‹ํ•˜๊ณ  ๊ฐ ๋‹จ๋ฐฑ์งˆ์˜ ํŠน ์„ฑ์„ ๋ฌธ์„œ์—์„œ ์ž๋™์œผ๋กœ ํŒŒ์•…ํ•˜์—ฌ ๊ธฐ์กด์˜ ์˜จํ†จ๋กœ์ง€์™€ ์—ฐ๊ณ„์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์˜จํ†จ๋กœ ์ง€ ์šฉ์–ด๊ฐ€ ๋ฌธ์„œ์—์„œ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋กœ ๋ฐœ๊ฒฌ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์ด๋“ค์„ ๋…ผ๋ฆฌ์  ํ‘œํ˜„์œผ๋กœ ์ž๋™ ๋ณ€ํ™˜ํ•˜ ๊ณ , ๋ฌธ์„œ์—์„œ ๋‹จ๋ฐฑ์งˆ์˜ ํŠน์„ฑ์„ ์„ค๋ช…ํ•˜๋Š” ๋ฌธ์žฅ๋“ค์„ ์ถ”์ถœ ๋ฐ ๋ถ„์„ํ•˜์—ฌ ์˜จํ†จ๋กœ์ง€ ์šฉ์–ด์˜ ๋…ผ๋ฆฌ ์  ํ‘œํ˜„๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋ฌธ์„œ์—์„œ ๋‹จ๋ฐฑ์งˆ ํŠน์„ฑ์„ ์ธ์‹ํ•  ๋•Œ, ์•ฝ์–ด ์ฒ˜๋ฆฌ ๋ฐ ์กฐ์‘ ํ˜„์ƒ ํ•ด๊ฒฐ ๋“ฑ ์˜ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค.

Morphological Analysis of Irregular Conjugation in Korean with Micro Combinatory Categorial Grammar

Ho-Joon Lee and Jong C. Park
Proceedings of the KISS Spring Conference, pp. 531-533, 2003.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ˜•ํƒœ์†Œ ์ˆ˜์ค€์˜ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ํ˜•ํƒœ์†Œ ๋ถ„์„์„ ํฌํ•จํ•œ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ์˜ ์—ฌ๋Ÿฌ ๋‹จ๊ณ„๋ฅผ ํ•œ ๋‹จ๊ณ„์˜ ์œ ๋„๊ณผ์ •์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๊ณ  ํ˜•ํƒœ์†Œ ๋ถ„์„ ๋‹จ๊ณ„์—์„œ ์ฆ๊ฐ€ํ•˜๋Š” ์• ๋งค์„ฑ๊ณผ ๋ณต์žก๋„๋ฅผ ์ƒ์œ„ ๋ถ„์„ ๋‹จ๊ณ„์˜ ์ •๋ณด ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ๋…ผํ•œ๋‹ค. ํ•œ๊ตญ์–ด์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ณต์žกํ•œ ์–ธ์–ด ํ˜„์ƒ ์ค‘์— ํ•˜๋‚˜์ธ ์šฉ์–ธ์˜ ๋ถˆ๊ทœ ์น™ ํ™œ์šฉ์„ ํ™•๋ฅ  ์ •๋ณด ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์Œ์šด์ •๋ณด๋ฅผ ํฌํ•จํ•œ ํ†ต์‚ฌ ์ •๋ณด๋‚˜ ์˜๋ฏธ ์ •๋ณด ๋“ฑ์˜ ์ƒ์œ„ ์ •๋ณด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ฒ˜๋ฆฌ ํ•˜์—ฌ๋ณด๊ณ  ์ผ๋ฐ˜์ ์ธ ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ๋กœ์„œ์˜ ๋ฐœ์ „ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด์„œ ์•Œ์•„๋ณธ๋‹ค.

Word Segmentation for Korean with Syllable-Level Combinatory Categorial Grammar

Ho-Joon Lee and Jong C. Park
Proceedings of the 14th National Conference on Korean Language Processing, pp. 47-54, October, 2002.
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ํ•œ๊ตญ์–ด์˜ ๋„์–ด์“ฐ๊ธฐ ํ˜„์ƒ์€ ๋‹จ์–ด๋ณ„๋กœ ์ •ํ˜•ํ™”๋œ ๋„์–ด์“ฐ๊ธฐ๋ฅผ ํ•˜๋Š” ์˜์–ด๋‚˜ ๋„์–ด์“ฐ๊ธฐ๊ฐ€ ๋ฐœ๋‹ฌํ•˜์ง€ ์•Š์€ ์ค‘ ๊ตญ์–ด, ์ผ๋ณธ์–ด์™€๋Š” ๋‹ค๋ฅด๊ฒŒ ๋…ํŠนํ•œ ํ˜•ํƒœ๋กœ ๋ฐœ์ „๋˜์–ด ์™”๋‹ค. ๊ธฐ์กด์—๋Š” ๋ถ€๋ถ„์ ์ธ ๋„์–ด์“ฐ๊ธฐ ์˜ค๋ฅ˜๋ฅผ ๋ฐ”๋กœ์žก ์•„์ฃผ๋Š” ํ˜•ํƒœ์˜ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜์—ˆ์ง€๋งŒ ์ด์ œ๋Š” ๋ฌธ์ž์ธ์‹์ด๋‚˜ ์Œ์„ฑ์ธ์‹ ๋“ฑ์˜ ์—ฐ๊ตฌ์™€ ๊ฒฐํ•ฉํ•˜์—ฌ ๋„์–ด ์“ฐ๊ธฐ๊ฐ€ ์™„์ „ํžˆ ๋ฌด์‹œ๋œ ๋ฌธ์žฅ์˜ ๋„์–ด์“ฐ๊ธฐ๋ฅผ ์ž๋™์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰ ์ค‘์ด ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด์˜ ๋„์–ด์“ฐ๊ธฐ ํ˜„์ƒ๊ณผ ๋„์–ด์“ฐ๊ธฐ ๋ณต์› ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์— ๋Œ€ํ•ด์„œ ์‚ด ํŽด๋ณด๊ณ  ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์ฒ˜๋ฆฌํ•˜๊ธฐ ํž˜๋“ค์—ˆ๋˜ ํ˜•ํƒœ๋ฅผ ์Œ์ ˆ๋‹จ์œ„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์œผ๋กœ ์„ค๋ช…ํ•œ๋‹ค.

Diphone-based Intonation and VoiceXML Document Generation using Multi-Dimensional Linguistic Information

Lee Hwa Jin and Jong C. Park
Proceedings of the 24th National Conference on Korean Language Processing, pp. 69-76, Cheongju, Korea, October, 2002.

Anaphora Resolution for Contextually Appropriate Animation of Multimedia Fairy Tales

Kyung Wha Hong and Jong C. Park
Proceedings of the 24th National Conference on Korean Language Processing, pp. 317-324, Cheongju, Korea, October, 2002.
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์ฐธ์กฐํ˜„์ƒ์ด๋ž€ ์ด๋ฏธ ์–ธ๊ธ‰๋˜์—ˆ๋˜ ํ˜น์€ ์ด๋ฏธ ์•Œ๊ณ  ์žˆ๋‹ค๊ณ  ์—ฌ๊ฒจ์ง€๋Š” ์ •๋ณด์— ๋Œ€ํ•œ ์žฌํ‘œํ˜„์ด๋‹ค. ์ฐธ์กฐํ˜„์ƒ์€ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ๋ถ„์•ผ์—์„œ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ์ง€๊ณผํ•™, ์‹ฌ๋ฆฌํ•™, ์ฒ ํ•™๋ถ„์•ผ์—์„œ๋„ ํ™œ๋ฐœํ•˜ ๊ฒŒ ์—ฐ๊ตฌ๋˜๋Š” ํ˜„์ƒ์œผ๋กœ ์ฐธ์กฐํ‘œํ˜„์ธ ์กฐ์‘์‚ฌ(anaphora)์˜ ์„ ํ–‰์‚ฌ(antecedent)๋ฅผ ์ฑ„ํƒํ•˜๋Š” ๋ฐฉ ๋ฒ•์— ๋”ฐ๋ผ ๊ทธ ์„ฑ๋Šฅ์ด ์ขŒ์šฐ๋œ๋‹ค. ์ž์—ฐ์–ธ์–ด๋ฌธ์žฅ์œผ๋กœ๋ถ€ํ„ฐ ๋ฉ€ํ‹ฐ๋™ํ™”๋ฅผ ์ƒ์„ฑ์„ ์œ„ํ•œ ์• ๋‹ˆ๋ฉ”์ด์…˜ ์ œ์–ด ์Šคํฌ๋ฆฝํŠธ ๋ช…๋ น๋“ค์—์„œ์˜ ์ฐธ์กฐํ•ด๊ฒฐ์€ ์„ ํ–‰ ์ •๋ณด์˜ ์ ์ ˆํ•œ ์ฐธ์กฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ž์—ฐ์Šค๋Ÿฌ์šด ์• ๋‹ˆ๋ฉ”์ด์…˜ ์žฅ๋ฉด์„ ์ƒ์„ฑํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ํ•„์ˆ˜์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋™ํ™”์˜ ์ž์—ฐ์–ธ์–ด ๋ฌธ์žฅ์— ๋‚˜ํƒ€๋‚˜๋Š” ์ฐธ์กฐํ˜„์ƒ๋“ค์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด๊ณ  ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ฐธ์กฐํ˜„์ƒ์„ ํ•ด๊ฒฐํ•˜ ๋Š” ๋ฐฉ๋ฒ•๊ณผ ๊ตฌํ˜„๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๋…ผ์˜ํ•œ๋‹ค.

Analysis and Reconstruction of Temporal Relations in Multimedia Fairy Tales for Digital Cinematography

Semin Jang and Jong C. Park
Proceedings of the 24th National Conference on Korean Language Processing, pp. 309-316, Cheongju, Korea, October, 2002.
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๋™ํ™”๋Š” ์‚ฌ๊ฑด์˜ ํ๋ฆ„์— ๋”ฐ๋ผ์„œ ์ด์•ผ๊ธฐ๋ฅผ ์ง„ํ–‰์‹œํ‚จ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋…์ž์ธ ์–ด๋ฆฐ์ด๋“ค์˜ ๊ด€์‹ฌ์„ ์ง€ ์†์ ์œผ๋กœ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‚ฌ๊ฑด์„ ์‹ค์ œ ์ˆœ์„œ์™€ ๋‹ค๋ฅด๊ฒŒ ๋ฐฐ์น˜ํ•ด๋†“์•„ ๊ทน์  ํšจ๊ณผ๋ฅผ ๊พ€ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์ด ์žˆ๋‹ค. ๋™ํ™”๋ฅผ ์• ๋‹ˆ๋ฉ”์ด์…˜์œผ๋กœ ์ƒ์„ฑํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ์ด๋Ÿฌํ•œ ์‚ฌ๊ฑด์˜ ๋ฐฐ์น˜์— ๋‹ด๊ธด ์ž‘๊ฐ€์˜ ์˜๋„๋ฅผ ์ œ๋Œ€๋กœ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์ฒ˜๋Ÿผ ์‚ฌ๊ฑด์˜ ํ ๋ฆ„์„ ํŒŒ์•…ํ•˜๊ณ  ์ด๋ฅผ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋‹ค๋ฃจ์–ด์•ผ ํ•  ์–ธ์–ด์  ์š”์†Œ๋“ค์— ๋Œ€ํ•˜์—ฌ ์‚ดํŽด๋ณด๊ณ , ๊ฒฐ ํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋™ํ™”์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์‹œ๊ฐ„ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋˜ํ•œ ๊ฐ ์‹œ๊ฐ„ ๊ด€๊ณ„์— ๋”ฐ๋ผ ์• ๋‹ˆ๋ฉ”์ด์…˜ ํšจ๊ณผ๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ์˜์ƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹œ๊ฐ„ ๊ด€๊ณ„๋ฅผ ์žฌํ˜„ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์„ค๋ช…ํ•œ๋‹ค.

Automatic Gene Ontology Extension and Terminology Analysis

Jin-Bok Lee and Jong C. Park
Proceedings of the KISS Conference, pp. 229-231, Suwon, Korea, October, 2002.
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์ƒ๋ฌผํ•™ ๋ถ„์•ผ์˜ ๋ฐฉ๋Œ€ํ•œ ์ง€์‹์„ ํšจ์œจ์ ์œผ๋กœ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•˜์—ฌ ์ƒ๋ฌผ์ •๋ณดํ•™์ด ์ฃผ์š”ํ•œ ์—ฐ๊ตฌ ๋ถ„์•ผ๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด ์ค‘ ํŠนํžˆ ์ƒ๋ฌผํ•™ ๋ฌธํ—Œ์—์„œ ์ •๋ณด๋ฅผ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ์ •๋ณด์ถ”์ถœ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ ์œ ์ „์ž ์˜จํ†จ๋กœ์ง€์™€ ๊ฐ™์€ ์œ ์šฉํ•œ ์ง€์‹๋ฒ ์ด์Šค๋ฅผ ์ž๋™์œผ๋กœ ํ™•์žฅํ•จ์œผ๋กœ์จ ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ ํ•˜๋Š” ์ƒ๋ฌผํ•™ ๋ถ„์•ผ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์„ ์ง€์‹๋ฒ ์ด์Šค์— ํ†ตํ•ฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ž๋™์œผ๋กœ ํ™•์žฅ๋œ ์˜จํ†จ๋กœ์ง€๋Š” ์‹ ๋ขฐ์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ๊ฒ€์ฆ ๊ณผ์ •์„ ๊ฑฐ์ณ, ์ •๋ณด์ถ”์ถœ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ง€์‹๋ฒ ์ด์Šค๋กœ ์‚ฌ์šฉ๋˜๊ฒŒ ๋œ ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹จ๋ฐฑ์งˆ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์กฐ๊ฑด์„ ์ถ”์ถœํ•˜๋Š” ์‹œ์Šคํ…œ๊ณผ ์œ ์ „์ž ์˜จํ†จ๋กœ์ง€๋ฅผ ์ด ์šฉํ•˜์—ฌ ์ถ”์ถœ๋œ ์ƒ๋ฌผํ•™ ์šฉ์–ด๋ฅผ ๋ถ„์„ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ  ์œ ์ „์ž ์˜จํ†จ๋กœ์ง€์˜ ์ž๋™ ํ™•์žฅ ๋ฐ ๊ฒ€์ฆ ์‹œ์Šคํ…œ ์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค.

Natural Language Query Interpretation System for Biomedical Database Access

Hodong Lee and Jong C. Park
Proceedings of the KISS Spring Conference, pp. 487-489, Han Yang University, April 26-27, 2002.
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๋ณธ ๋…ผ๋ฌธ์€ ์ด์งˆ์ ์ธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์— ์„ ์žฌ๋˜์–ด ์žˆ๋Š” ์ƒ๋ฌผ์˜๋ฃŒ ์ •๋ณด์˜ ๊ฐœ๋…์ ์ธ ์ ‘๊ทผ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•œ ์ž์—ฐ์–ธ์–ด์งˆ์˜ ์‹œ์Šคํ…œ์„ ์„ค๋ช…ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ์‹œ์Šคํ…œ์—์„œ๋Š” ์งˆ์˜๋ฌธ์„ SQL, OQL, CPL ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์ •ํ˜•์–ธ์–ด๋กœ ๋ณ€ํ™˜ํ•˜๋Š”๋ฐ, ์ด ๊ณผ์ •์—์„œ ํ•„์š”ํ•œ ์งˆ์˜๋ฌธ์˜ ๋ถ„์„ ๋ฐ ๋ณ€ํ™˜๊ณผ์ •์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๊ตฌ๋ฌธ๋ถ„์„์— ์˜ํ•ด ๋„์ถœ๋œ ์ •๋ณด๋ฅผ ์ด์šฉํ•ด ์ง์ ‘ ๋‹ค์–‘ํ•œ ์ •ํ˜•์–ธ์–ด๋“ค๋กœ ๋ณ€ํ™˜ํ•˜๋ฏ€๋กœ, ์‹œ์Šคํ…œ์˜ ๊ตฌ์กฐ๊ฐ€ ๊ฐ„๊ฒฐํ•ด์ง€๊ณ  ๋ชจ๋“ˆํ™”๋˜์–ด ์ „์ฒด ์„ฑ๋Šฅ๊ณผ ์ด์‹์„ฑ์˜ ํ–ฅ์ƒ์„ ๊ฐ€์ ธ์˜ฌ ์ˆ˜ ์žˆ๋‹ค.

Challenges in Biopathway Extraction from Literature and Ontology Building for Biology

Jong C. Park
Korea Society for Bioinformatics Workshop, February, 2002.

Semi-Automatic Extension of Gene Ontology

Jin-Bok Lee, Jung-jae Kim, and Jong C. Park
Human Computer Interaction (HCI) Workshop, Phoenix Park, Korea, January, 2002.

BiopathwayBuilder: Nested 3D visualization system for complex molecular interactions

Changsu Lee, Jinah Park, and Jong C. Park
Proceedings of International Conference on Genome Informatics (GIW), pp. 447-448, Tokyo, Japan, 2002.
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In order to gain a full understanding of a biological process, we must be able to augment the known molecular interactions with discovered knowledge. We believe that a visualization system works as a means for accomplishing this task, as it provides an intuitive base for necessary information, among others. However, reported implementations have further problems: (1) The size of the information is not only enormous, but also grows very fast, which makes scalability and elision essential properties [5]; (2) the available information is not only incomplete, but also unreliable; and (3) the usual information in the field, such as protein modification [2], is inherently complex, which makes it very difficult to make the resulting visualization intuitive enough for end users as well as field experts. We address all the problems above with a 3D visualization system.

3D Visualization System for Complex Protein-Protein Interactions from Text Data Mining

Changsu Lee, Jinah Park, and Jong C. Park
IEEE Workshop on Visualization in Bioinformatics and Cheminformatics, Boston, USA, 2002.

Natural Language Interpretations for Heterogeneous Database Access

Hodong Lee and Jong C. Park
Proceedings of the International Conference on Computational Linguistics (COLING), pp. 523-529, Taiwan, 2002.

Text Data Mining for Automatic Gene Ontology Extension

Jin-Bok Lee and Jong C. Park
Intelligent Systems for Molecular Biology (ISMB), Proceedings of the second meeting of the special interest group on Text Data Mining, pp. 22-25, Edmonton, Alberta, Canada, 2002.

Literature Data Mining for Biology

Lynette Hirschman, Jong C. Park, Junichi Tsujii, Cathy Wu, and Limsoon Wong
Proceedings of the Pacific Symposium on Biocomputing (PSB) session, pp. 323-325, Hawaii, USA, 2002.

Natural Language Processing for Biomedical Information Extraction and Automatic Ontology Management

Jong C. Park
Proceedings of the 2nd Bioinformatics Forum, pp. 145-158, Seoul, Korea, 2002.

Biomedical Informatics and Natural Language Processing

Jong C. Park
Annual Meeting of the Korean Society for Medical Informatics, Jeon-ju, Korea, November, 2001.

Automatic Augmentation of Translation Dictionary with Database Terminologies in Multilingual Query Interpretation

Hodong Lee and Jong C. Park
Annual Meeting of the Association for Computational Linguistics (ACL), Workshop on Human Language Technologies and Knowledge Management, pp. 113-120, Toulouse, France, July, 2001.
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In interpreting multilingual queries to databases whose domain information is described in a particular language, we must address the problem of word sense disambiguation. Since full-fledged semantic classification information is difficult to construct either automatically or manually for this purpose, we propose to disambiguate the senses of the source lexical items by automatically augmenting a simple translation dictionary with database terminologies and describe an implemented multilingual query interpretation system in a combinatory categorial grammar framework.

Translating Natural Language Queries into Formal Language Queries with Combinatory Categorial Grammar

Hodong Lee and Jong C. Park
Proceedings of the International Conference on Computer Processing of Oriental Languages (ICCPOL), pp. 41-46, Seoul, Korea, May, 2001.

Computational Generation of Context-based Intonation for Korean with Combinatory Categorial Grammar

Lee Hwa Jin and Jong C. Park
Proceedings of International Conference on Computer Processing of Oriental Languages (ICCPOL), pp. 415-420, Seoul, Korea, May, 2001.

Design and Implementation of E-Mail Response Management System for Call Center

Jung-jae Kim, O Shik Kwon, Hodong Lee, and Jong C. Park
Proceedings of the KISS Spring Conference, pp. 445-447, April, 2001.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ฝœ์„ผํ„ฐ๋ฅผ ์œ„ํ•˜์—ฌ ์„ค๊ณ„ ๋ฐ ๊ตฌํ˜„๋œ ์ „์ž๋ฉ”์ผ ์ž๋™์‘๋‹ต ๋ฐ ๊ด€๋ฆฌ ์‹œ์Šคํ…œ ์ค‘์—์„œ ์„œ๋ฒ„ ์‹œ์Šคํ…œ์— ํ•ด๋‹นํ•˜๋Š” ๋ถ€๋ถ„์„ ๊ธฐ์ˆ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋„๋ฉ”์ธ์— ํŠน์„ฑํ™”๋œ ํ‘œํ˜„ ํ˜•์‹ ๊ฐœ๋ฐœ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋ณด๋‹ค ํšจ์œจ์ ์ธ 3๋‹จ๊ณ„ ๋งค์นญ๋ฐฉ๋ฒ•์„ ๊ฐ€์ง„ ์ž๋™์‘๋‹ต๊ธฐ, ํ•™์Šต์— ๊ธฐ๋ฐ˜ํ•œ ๋„๋ฉ”์ธ ๋น„์˜์กด์ ์ธ ์ž๋™๋ถ„๋ฅ˜๊ธฐ ๋ฐ ์ ์šฉ๋ฐฅ๋ฒ™์˜ ์žฌ๋ฐฐ์—ด์ด ๊ฐ€๋Šฅํ•œ ๋‹ด๋‹น์ž ๋ถ„๋ฐฐ๊ธฐ๋ฅผ ๊ตฌํ˜„ํ•˜์˜€๋‹ค.

Bidirectional Incremental Parsing for Automatic Pathway Identification with Combinatory Categorial Grammar

Jong C. Park, Hyun Sook Kim, and Jung-jae Kim
Pacific Symposium on Biocomputing (PSB), pp. 396-407, Big Island, Hawaii, USA, January, 2001.
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As the importance of automatically extracting and analyzing various natural language assertions about protein-protein interactions in biomedical publications is recognized, many uses of natural language processing techniques are proposed in the literature. However, most proposals to date make rather simplifying assumptions about the syntactic aspects of natural language due to various reasons including efficiency. In this paper, we describe an implemented system that utilizes combinatory categorical grammar known to be competent in modeling natural language, with a controlled mechanism for the parser to operate bidirectionally and incrementally. We discuss the performance of the system on a large set of abstracts in Medline with quite encouraging results.

Real Time Synthesis of Multimedia Tales in Korean with Combinatory Categorial Grammar

Hyun Sook Kim and Jong C. Park
Proceedings of the National Conference on Korean Information Processing, pp. 509-512, 2001.

Computational Processing of Honorifics in Korean with Combinatory Categorial Grammar

O Shik Kwon and Jong C. Park
Proceedings of the National Conference on Korean Information Processing, pp. 365-372, 2001.
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ํ•œ๊ตญ์–ด๋‚˜ ์ผ๋ณธ์–ด๋Š” ์˜์–ด ๋“ฑ ์„œ๊ตฌ์˜ ์–ธ์–ด์™€ ๋น„๊ตํ•˜์—ฌ ๋งค์šฐ ๋ฐœ๋‹ฌ๋œ ๊ฒฝ์–ด ์ฒด๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ฒฝ์–ด ์ฒด๊ณ„๋Š” ์ด๋“ค ์–ธ์–ด๋ฅผ ๋ชจ๊ตญ์–ด๋กœ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ์‚ฌ๋žŒ๋“ค์„ ํฌํ•จํ•˜์—ฌ ๋ชจ๊ตญ์–ด๋กœ ์‚ฌ์šฉํ•˜๋Š” ๋งŽ์€ ์‚ฌ๋žŒ๋“ค๊นŒ์ง€๋„ ์ •ํ™•ํ•˜๊ฒŒ ๊ตฌ์‚ฌํ•˜๊ธฐ๋Š” ์–ด๋ ค์›Œ ํ•˜๋Š” ๊ฒƒ์ด ํ˜„์‹ค์ด๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ฒฝ์–ด ์ฒด๊ณ„์˜ ์ •ํ™•ํ•œ ๊ตฌ์‚ฌ ๋Šฅ๋ ฅ์€ ์ ์ ˆํ•œ ์–ดํœ˜ ์„ ํƒ ๋Šฅ๋ ฅ๊ณผ ํ•จ๊ป˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์˜์‚ฌ ์†Œํ†ต์„ ์œ„ํ•œ ์ค‘์š”ํ•œ ์–ธ์–ด ๋Šฅ๋ ฅ์œผ๋กœ ๊ฐ„์ฃผ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๊ธฐ๊ณ„๋ฒˆ์—ญ๊ธฐ๋‚˜ ๋ฌธ๋ฒ•๊ฒ€์‚ฌ๊ธฐ๋ฅผ ๊ตฌํ˜„ํ•˜๊ณ ์ž ํ•  ๋•Œ ์ด๋Ÿฌํ•œ ๊ฒฝ์–ด ์ฒด๊ณ„๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ดํ•ดํ•˜๋Š” ์‹œ์Šคํ…œ์˜ ๊ตฌํ˜„์€ ํ•œ ์ฐจ์› ๋†’์€ ์ž์—ฐ์Šค๋Ÿฌ์šด ํ‘œํ˜„์„ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ•„์ˆ˜์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด์˜ ๊ฒฝ์–ด ์ฒด๊ณ„๋ฅผ ์กฐ์‚ฌํ•˜๊ณ  ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์ด๋ฅผ ๊ฒ€์ฆํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์†Œ๊ฐœํ•œ ๋’ค ์‚ฌ๊ทน ๋Œ€๋ณธ์„ ๋Œ€์ƒ์œผ๋กœ ์ด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ํ™•์ธํ•œ๋‹ค.

Generation of Contextually Appropriate Responses in E-Commerce with Combinatory Categorial Grammar

Jin-Bok Lee and Jong C. Park
Proceedings of the Human Computer Interaction (HCI) Symposium, pp. 314-319, Phoenix Park Convention Center, Korea, 2001.
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We analyze various constructions in Korean including coordination, relative clauses, and embedded clauses by focusing on the phenomenon of quantifier floating where quantifying expressions may appear in places other than their original prenominal one. Based on these analyses, we process Korean sentences in a combinatory categorial grammar (CCG) framework that makes use of all the levels of syntax, semantics, and discourse. Finally, we describe an implemented query system that generates responses with contextually appropriate ellipsis in the domain of e-commerce.

Processing Floating Quantifiers with Combinatory Categorial Grammar

Jin-Bok Lee and Jong C. Park
the KISS Regional Conference, November, 2000.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์–‘ํ™”์‚ฌ์œ ๋™์„ ๋ณ‘๋ ฌ๊ตฌ๋ฌธ, ๊ด€๊ณ„๊ตฌ๋ฌธ, ๋‚ดํฌ๊ตฌ๋ฌธ๊ณผ ๊ฐ™์ด ๋ณต์žกํ•œ ์–ธ์–ดํ˜„์ƒ๊ณผ ๊ด€๋ จํ•˜์—ฌ ํ†ต์‚ฌ์ , ์˜๋ฏธ์ , ๋‹ดํ™”์  ๊ด€์ ์—์„œ ๊ณ ๋ คํ•˜๊ณ , ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ž์ƒ๊ฑฐ๋ž˜์™€ ๊ฐ™์€ ๋ถ„์•ผ์—์„œ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋Œ€ํ™”๋ฅผ ํ•  ์ˆ˜ ์žˆ๋Š” ์ธํ„ฐํŽ˜์ด์Šค ๊ตฌ์ถ•์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค.

Predicting Contextually Appropriate Intonation from Utterances in Korean with Combinatory Categorial Grammar

Lee Hwa Jin and Jong C. Park
Proceedings of the National Conference on Korean Language Processing, pp. 68-75, October, 2000.
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์ƒ๋Œ€๋ฐฉ์—๊ฒŒ ์˜์‚ฌ๋ฅผ ์ „๋‹ฌํ•  ๋•Œ ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ฒŒ ์ž์‹ ์˜ ์˜๋„๋ฅผ ํ‘œํ˜„ํ•˜๋ ค๋ฉด ๋Œ€ํ™”์˜ ํ๋ฆ„์— ๋งž๋Š” ์ ์ ˆํ•œ ์–ต์–‘์„ ์ฃผ์–ด ๋ฐœํ™”ํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋ฌธ์žฅ์„ ๋ถ„์„ํ•˜๊ณ  ๋ฌธ์žฅ ๋‚ด ์ •๋ณด์™€ ๋ฌธ์žฅ ๊ฐ„ ์ •๋ณด ์ฆ‰, ๋ฌธ๋งฅ์— ๋”ฐ๋ผ ๊ฐ•์„ธ(pitch accent), ํœด์ง€(pause), ๊ฐ•์กฐ ๋“ฑ์˜ ์–ต์–‘์ •๋ณด๋ฅผ ์–ด๋–ป๊ฒŒ ๋‚˜ํƒ€๋‚ด์•ผ ํ•˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์—ฌ ๋ฌธ์žฅ์˜ ์ •๋ณด๊ตฌ์กฐ์— ์ถ”๊ฐ€ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค.

Combinatory Categorial Grammar and Natural Language Interface to Database

Hodong Lee and Jong C. Park
Proceedings of the Human-Computer Interaction (HCI) Triangle Workshop, pp. 900-905, Phoenix Park Convention Center, Korea, January, 2000.
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In this paper, we discuss issues related to the construction of a natural language interface to databases, including the characteristics of natural language queries. We propose to implement the system using Combinatory Categorial Grammar (CCG), so that various linguistic phenomena can be handled incrementally and in a modular manner for diverged expressions.

Informed Parsing for Coordination with Combinatory Categorial Grammar

Jong C. Park and Hyung-joon Cho
Proceedings of the International Conference on Computational Linguistics (COLING), pp. 593-599, Saarbrucken, Germany, 2000.
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Coordination in natural language hampers efficient parsing, especially due to the multiple and mostly unintended candidate conjuncts/disjuncts in a given sentence that shows structural ambiguity. The problem gets more serious in a combinatory categorial grammar framework, which is well known for its competent treatment of coordination, as the flexibility of syntactic analysis often strikes back as spurious ambiguity. We propose to address these ambiguities with predicate argument structures and semantic co-occurrence similarity information, and present encouraging results.

Combinatory Categorial Grammar for Natural Language Interface

Hodong Lee and Jong C. Park
Proceedings of the KISS Fall Conference, pp. 173-175, 2000.
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๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „์ž์ƒ๊ฑฐ๋ž˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์ด์šฉํ•œ ์ž์—ฐ์–ธ์–ด์งˆ์˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ๊ตฌํ˜„ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์งˆ์˜๋ฌธ์„ ๋ถ„์„ํ•˜๊ณ  ํ‘œํ˜„ ๋ฐฉ๋ฒ•์„ ๋…ผ์˜ํ•œ๋‹ค. ๋˜ํ•œ SQL ํ˜•์‹์–ธ์–ด๋กœ ๋ณ€ํ™˜ํ•˜๊ธฐ ์œ„ํ•œ ์–ดํœ˜ ํ‘œํ˜„ ๋ฐ ์œ ๋„ ๋ฐฉ๋ฒ•์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๊ตฌ๋ฌธ๋ถ„์„ ๊ณผ์ •์—์„œ SQL ํ˜•์‹์˜ ์งˆ์˜๋ฌธ์„ ์ง์ ‘ ์œ ๋„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆ๋๋˜ ์ค‘๊ฐ„๋…ผ๋ฆฌ์–ธ์–ด ๋ณ€ํ™˜๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜์ง€ ์•Š์œผ๋ฏ€๋กœ ๊ณผ์ •์ด ๊ฐ„๊ฒฐํ•ด์ ธ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅํ–ฅ์ƒ์„ ๊ฐ€์ ธ์˜ฌ ์ˆ˜ ์žˆ๋‹ค. ์‹œ์Šคํ…œ์€ ์›น ๊ธฐ๋ฐ˜๊ณผ client/server ๊ตฌ์กฐ๋กœ ๊ตฌํ˜„๋œ๋‹ค.

Combinatory Categorial Grammar and Parsing

Hyung-joon Cho and Jong C. Park
Proceedings of the National Conference on Korean Language Processing, pp. 223-230, Mokpo, Korea, October 1999.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์œผ๋กœ ํ•œ๊ตญ์–ด๋ฅผ ์ฒ˜๋ฆฌํ•  ๋•Œ ๊ตฌ๋ฌธ๋ถ„์„๊ณผ์ •์—์„œ ๋ณต์žก๋„๋ฅผ ๋†’์ด๋Š” ์—ญํ• ์„ ํ•˜๋Š” spurious ambiguity์™€ ๊ตฌ์กฐ์  ๋ชจํ˜ธ์„ฑ์ด ์žˆ๋Š” ๋ช…์‚ฌ๊ตฌ ์ ‘์†์— ๋Œ€ํ•ด์„œ ๋…ผํ•œ๋‹ค. ํ†ต์‚ฌ์  ์ฒ˜๋ฆฌ์™€ ์˜๋ฏธ์  ์ฒ˜๋ฆฌ๊ฐ€ ๋™์‹œ์— ์ˆ˜ํ–‰๋˜๋Š” ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์˜ ํŠน์ง•์„ ์‚ฌ์šฉํ•ด์„œ spurious ambiguity๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋ณต์žก๋„๋ฅผ ์ค„์ด๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ  ์ ‘์†ํ•ญ์—์„œ ์ ‘์†์˜ ์ค‘์‹ฌ์ด ๋˜๋Š” ๋ช…์‚ฌ๋“ค ๊ฐ„์˜ ๊ณต๊ธฐ์œ ์‚ฌ๋„๋ฅผ ์ด์šฉํ•ด์„œ ์ ‘์†ํ•ญ ์„ ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ณต์žก๋„์™€ ์˜ค๋ถ„์„์„ ์ค„์ด๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ ๋’ค ์ด์˜ ๊ฐœ์„ ๋ฐฉ์•ˆ์„ ๋…ผ์˜ํ•œ๋‹ค.

An Analysis of the Semantic and Discourse Functions of the Korean Special Marker `-to'

June K. Park and Jong C. Park
the National Conference on Korean Language Processing, Mokpo, Korea, October 1999.
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๋ณธ ๋…ผ๋ฌธ์€ ํ•œ๊ตญ์–ด์˜ ํŠน์ˆ˜์กฐ์‚ฌ, ํŠนํžˆ '๋„'์˜ ์˜๋ฏธ, ๋ฌธ๋งฅ์  ๊ธฐ๋Šฅ์— ๋Œ€ํ•˜์—ฌ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. '๋„'๋Š” ๋ฌธ๋งฅ์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์—ฐ๊ฒฐ์— ์žˆ์–ด์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. '๋„'๊ฐ€ ์“ฐ์ธ ๋ฌธ์žฅ์˜ ๋ฐฐ๊ฒฝ์—๋Š” ๋ฐ˜๋“œ์‹œ ์ผ์ •ํ•œ ์ „์ œ๊ฐ€ ์กด์žฌํ•œ๋‹ค. ์ „์ œ๋Š” ๊ทธ ๋ฌธ์žฅ์˜ ์˜๋ฏธ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ธฐ์กด ๋ฌธ๋งฅ๊ณผ๋„ ์ง์ ‘์ ์œผ๋กœ ์—ฐ๊ด€๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” '๊ฐ™์Œ', '์œ ์‚ฌํ•จ', '๊ทนํ•œ', '์ฒจ๊ฐ€' ๋ฐ ๋ณ‘๋ ฌ๋ฌธ์—์„œ ์“ฐ์ด๋Š” ๋‹ค์„ฏ ๊ฐ€์ง€ '๋„'์˜ ๊ธฐ๋Šฅ์— ๋Œ€ํ•˜์—ฌ ์„ค๋ช…ํ•˜๊ณ , alternatives semantics๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด๋ฅผ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•(CCG)์—์„œ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค.

A CCG for Coordination in Korean

Hyung-joon Cho and Jong C. Park
Proceedings of the KISS Conference, pp. 327-329, Jeonju, Korea, April, 1999.
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์ž์—ฐ์–ด์ฒ˜๋ฆฌ์— ์žˆ์–ด์„œ ๋ณ‘๋ ฌ๋ฌธ์€ ๋ถ„์„์˜ ๋ณต์žก์„ฑ, ๋‹จ์–ด์˜ ๋ชจํ˜ธ์„ฑ, ๊ณต๋ฐฑ ๋“ฑ์— ๋”ฐ๋ฅธ ์–ด๋ ค์›€์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์— ์ œ์‹œ๋˜์—ˆ๋˜ ํ•œ๊ตญ์–ด ์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•œ ๋ฒ”์ฃผ๋ฌธ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๋…ผํ•˜๊ณ  ๊ธฐ์กด์˜ ๋ฒ”์ฃผ๋ฌธ๋ฒ•๋“ค์ด ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ–ˆ๋˜ ํ•œ๊ตญ์–ด ๋ณ‘๋ ฌ๋ฌธ์„ ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์„ ์‚ฌ์šฉํ•ด์„œ ํ•ด๊ฒฐํ•œ๋‹ค. ํ•œ๊ตญ์–ด ๋ณ‘๋ ฌ๋ฌธ์„ ์ฒ˜๋ฆฌํ•˜๋Š” ๊ณผ์ •์—์„œ ๋น„ํ˜•์ƒ์–ธ์–ด์ธ ํ•œ๊ตญ์–ด ๋ณ‘๋ ฌ๋ฌธ์„ ์„œ์ˆ ๋…ผํ•ญ ๊ตฌ์กฐ๋กœ ํ‘œํ˜„ํ•˜๊ณ  ์ด๋ฅผ ๊ธฐ๊ณ„๋ฒˆ์—ญ์‹œ์Šคํ…œ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.

Multiset-CCG for Quantifier Floating in Korean

Jin-Bok Lee and Jong C. Park
Proceedings of the KISS Conference, pp. 330-332, Jeonju, Korea, April, 1999.
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๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์–ด์—์„œ ์–‘ํ™”์‚ฌ๊ฐ€ ๋‚˜์˜ค๋Š” ์œ ํ˜•์„ ์‚ดํŽด๋ณด๊ณ , ๊ทธ ์ค‘์—์„œ QFํ˜„์ƒ์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค. QFํ˜„์ƒ์ด ์ฃผ๊ฒฉ, ๋ชฉ์ ๊ฒฉ, ์—ฌ๊ฒฉ์—์„œ ๋ชจ๋‘ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์ œ์‹œํ•˜๊ณ , ๋‚ดํฌ๋ฌธ์—์„œ์˜ QF๊ฐ€ ๊ฐ–๋Š” ์ œ์•ฝ์กฐ๊ฑด์„ ์„ค๋ช…ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒƒ๋“ค์„ ํ•œ๊ตญ์–ด ์ค‘์ง‘ํ•ฉ๊ฒฐํ•ฉ๋ฒ”์ฃผ๋ฌธ๋ฒ•์˜ framework์—์„œ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.

Lexical Selection with a Target Language Monolingual Corpus and an MRD

Hyun Ah Lee, Jong C. Park, and Gil Chang Kim
Proceedings of the Theoretical and Methodological Issues in Machine Translation (TMI), pp. 150-160, Chester, England, 1999.
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In this paper, we propose a lexical selection method with three steps: sense disambiguation of source words, sense-to-word mapping, and selection of the most appropriate target language lexical item. The knowledge for each step is extracted from a machine readable dictionary and a target language monolingual corpus. By splitting the process of lexical selection into three steps and extracting the essential knowledge for each step from existing resources, our system can select appropriate words for translation with high extensibility and robustness.

Checking Grammatical Mistakes for English-as-a-Second-Language (ESL) Students

Jong C. Park, Martha Palmer, and Gay Washburn
Proceedings of the KSEA-NERC, New Brunswick, New Jersey, USA, April, 1997.

An English Grammar Checker as a Writing Aid for Students of English as a Second Language

Jong C. Park, Martha Palmer, and Gay Washburn
Conference on Applied Natural Language Processing (ANLP), Descriptions of System Demonstrations and Videos, Washington, D.C., USA, March, 1997.
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We present a prototype grammar checker for English as a Second Language (ESL) students, utilizing Combinatory Categorial Grammar (CCG) written in SICStus Prolog. Instead of attempting to handle all possible grammatical errors, the grammar checker identifies certain specific types of grammatical mistakes that appear more regularly than others in the present domain of application.

Quantifier Scope and Constituency

Jong C. Park
The 33rd Annual Meeting of the Association for Computational Linguistics (ACL), Cambridge, Massachusetts, USA, June, 1995.
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Traditional approaches to quantifier scope typically need stipulation to exclude readings that are unavailable to human understanders. This paper shows that quantifier scope phenomena can be precisely characterized by a semantic representation constrained by surhce constituency, if the distinction between referential and quantificational NPs is properly observed. A CCG implementation is described and compared to other approaches.

Semantic Significance of Quantification in Natural Language Processing

Jong C. Park
Proceedings of the KSEA-NERC, pp. 432-436, New Brunswick, New Jersey, USA, March, 1995.

A Unification-based Semantic Interpretation for Coordinate Constructs

Jong C. Park
The 30th Annual Meeting of the Association for Computational Linguistics (ACL), Delaware, USA, June, 1992.
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This paper shows that a first-order unification-based semantic interpretation for various coordinate constructs is possible without an explicit use of lambda expressions if we slightly modify the standard Montagovian semantics of coordination. This modification, along with partial execution, completely eliminates the lambda reduction steps during semantic interpretation.