숫자로 표상된 의미: 딥러닝 시대의 의미론
Meaning in Numbers: Semantics in the Age of Deep Learning
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The advent of some practical Artificial Intelligence (AI) applications and the wide availability of Deep Learning algorithms seem to have shaken most aspects of everyday life. In particular the arrival of the vector space modelling based on word embedding, and the availability of the tools like Word2vec signalled the era of high quality word vectors and literally have changed the world of Natural Language Processing. In this paper we discuss the nature of the vector space model as an alternative in linguistic semantics. We also discuss some of its characteristics and limitations, and some possible related linguistic issues based on results gained from applying Word2vec to two of the well known Korean corpora, the Sejong Semantically Annotated Corpus and part of the Trend21 corpora.
Abstract
The advent of some practical Artificial Intelligence (AI) applications and the wide availability of Deep Learning algorithms seem to have shaken most aspects of everyday life. In particular the arrival of the vector space modelling based on word embedding, and the availability of the tools like Word2vec signalled the era of high quality word vectors and literally have changed the world of Natural Language Processing. In this paper we discuss the nature of the vector space model as an alternative in linguistic semantics. We also discuss some of its characteristics and limitations, and some possible related linguistic issues based on results gained from applying Word2vec to two of the well known Korean corpora, the Sejong Semantically Annotated Corpus and part of the Trend21 corpora.
참고문헌 (29)
- 김일환, "디지털 자료와 의미 연구의 다양성", 한국어 의미학, 56, pp.89~109 (2017), 한국어의미학회
- 남승호, "한국어 부정극어의 유형과 그 허가조건", 언어학, (22), pp.217~244 (1998), 사단법인 한국언어학회
- 시정곤, "국어의 부정극어에 대한 연구", 국어국문학, 119, pp.49~78 (1997), 국어국문학회
- 시정곤, "국어의 부정극어 허가조건", 언어, 22(3), pp.471~497 (1997), 한국언어학회
- 신준철, "기분석 부분 어절 사전을 활용한 한국어 형태소 분석기", 정보과학회논문지 : 소프트웨어 및 응용, 39(5), pp.415~424 (2012), 한국정보과학회[DOI]
- 양명희, "국어사전의 유의어에 대하여", 한국어 의미학, 22, pp.165~184 (2007), 한국어의미학회
- 옥철영, "세종 형태의미말뭉치: 형태분석 오류 수정 및 동형이의어 태깅" (2010), 울산대 한국어처리연구실
- 홍민표, "부정극어와 의문문의 의미와 화용", 언어연구, 27(3), pp.557~579 (2010), 언어정보연구소[DOI]
- 홍민표, "영어의미론" (2011), 종합출판ENG
- Baroni, M., "Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space", Conference on Empirical Methods in Natural Language Processing (EMNLP-10) (2010)
- Berners-Lee, Tim, "Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by its Inventor" (1999), Harper San Francisco
- Berners-Lee, T., "The semantic web", Scientific American, 284(5), pp.34~43 (2001)
- Chollet, F., "Deep Learning with R" (2018), Manning Publications Company
- Clark, Stephen, "The Handbook of Contemporary Semantic Theory", pp.493~522 (2015), Wiley-Blackwell
- Copestake, A., "Minimal Recursion Semantics: An introduction", Research on Language and Computation, 3, pp.281~332 (2005)
- Dowty, David R., "Introduction to Montague Semantics" (1981), Springer
- Hausser, Roland, "Database semantics for natural language", Artificial Intelligence, 130(1), pp.27~74 (2001)
- Katz, Jerrold, "The structure of a semantic theory", Language, 39, pp.170~210 (1963)
- Leech, Geoffrey, "Semantics: The Study of Meaning" (1981), Pelican
- Lyons, John, "Linguistic Semantics: An Introduction (Cambridge Approaches to Linguistics)" (1995), Cambridge University Press
- McEnery, T., "코퍼스 언어학-방법․이론․실제" (2018), 고려대출판문화원
- Mikolov, T., "Efficient estimation of word representations in vector space", Proceedings of Workshop at ICLR (2013)
- Pustejovsky, James, "The Generative Lexicon" (1995), MIT Press
- Schütze, Hinrich, "Proceedings of Advances in Neural Information Processing Systems 5", pp.895~902 (1993)
- Widdows, Dominic, "Geometry and Meaning" (2004), CSLI Publications
- "세종 형태의미말뭉치"[URL]
- "물결21"[URL]
- "Wikipedia", ~
- "도구: Word2vec, R, Python(gensim), Perl, Utagger", ~