토픽모델링을 이용한 코퍼스의 주제구조 탐색
Exploring the Thematic Structure in Corpora with Topic Modeling
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초록
This paper aims to demonstrate the applicability of topic modeling, which can organize and summarize large archives of texts, from a corpus-linguistic perspective. To do this, we investigate thematic structures in the Brown Corpus uncovered by an R package which implements topic modeling based on LDA (latent Dirichlet allocation), and use statistical techniques such as comparison cloud, principal component analysis and phylogenetic tree to analyze and visualize the results effectively. This paper shows (i) that the Brown Corpus has a core thematic structure which is divided into texts representing the tendency of past tense and spoken language and texts representing the tendency of present tense and written language, (ii) that the former texts are mainly about women, home, and battle, and the latter texts are primarily related to humanities, society and the economy, and (iii) that the linguistic texts reveal the interdisciplinary nature related to mathematics and engineering, as well as humanities and social sciences.
Abstract
This paper aims to demonstrate the applicability of topic modeling, which can organize and summarize large archives of texts, from a corpus-linguistic perspective. To do this, we investigate thematic structures in the Brown Corpus uncovered by an R package which implements topic modeling based on LDA (latent Dirichlet allocation), and use statistical techniques such as comparison cloud, principal component analysis and phylogenetic tree to analyze and visualize the results effectively. This paper shows (i) that the Brown Corpus has a core thematic structure which is divided into texts representing the tendency of past tense and spoken language and texts representing the tendency of present tense and written language, (ii) that the former texts are mainly about women, home, and battle, and the latter texts are primarily related to humanities, society and the economy, and (iii) that the linguistic texts reveal the interdisciplinary nature related to mathematics and engineering, as well as humanities and social sciences.
참고문헌 (26)
- 강범모, "한국어의 텍스트 장르와 언어특성" (1999), 고려대학교출판부
- 강범모, "통계적 방법에 의한 한국어 텍스트 유형 및 문체 분석", 언어학, (22), pp.3~57 (1998), 사단법인 한국언어학회
- 21세기 세종계획 기초자료구축 연구 보고서, "김흥규", 국립국어원, ~ (2007)
- Allan, J., "Topic models for summarizing novelty", ARDA Workshop on Language Modeling and Information Retrieval (2001)
- Atkins, S., "Corpus design criteria", Journal of Literary and Linguistic Computing, 7(1), pp.1~16 (1992)
- Biber, D., "Variation across Speech and Writing" (1988), Cambridge University Press
- Biber, D., "Grammar of Spoken and Written English" (1999), Longman
- Blei, D. M., "Probabilistic topic models", Communications of the ACM, 55(4), pp.77~84 (2012)
- Blei, D. M., "Latent Dirichlet allocation", Journal of Machine Learning Research, 3, pp.993~1022 (2003)
- Choe, Jae-Woong, "Interdisciplinary nature of linguistics as shown in the results of Topic Modelling on the Brown Corpus", Second Asia Pacific Corpus Linguistics Conference (2014), The Hong Kong Polytechnic University
- Expert Advisory Group on Language Engineering Standards, "Preliminary Recommendations on Text Typology", EAGLES Document EAGTCWG-TTYP, 1996
- Ferguson, C., "Sociolinguistic Perspectives on Register", pp.15~30 (1994), Oxford
- "A Standard Corpus of Present-Day Edited American English, for Use with Digital Computers" (1964)[URL]
- Griffiths, T., "Advances in Neural Information Processing Systems 17", pp.537~544 (2005), MIT Press
- Grün, B., "Topicmodels : An R package for fitting topic models", Journal of Statistical Software, 40(13), pp.1~30 (2011)
- Halliday, M. A. K., "Spoken and Written Language" (1989), Oxford University Press
- "Mallet: A Machine Learning for Language Toolkit" (2002)[URL]
- Newman, D. J., "Probabilistic topic decomposition of an eighteenth-century American newspaper", Journal of the American Society for Information Science and Technology, 57(6), pp.753~767 (2006)
- Rubin, T. N., "Statistical topic models for multi-label document classification", Machine learning, 88(1-2), pp.157~208 (2012)
- Sapir, E., "The status of linguistics as a science", Language, 5(4), pp.207~214 (1929)
- Savoy, J., "Authorship attribution based on a probabilistic topic model", Information Processing & Management, 49(1), pp.341~354 (2013)
- Scott, M., "Textual Patterns: Key Words and Corpus Analysis in Language Education" (2006), John Benjamins Publishing
- Swales, J., "Genre Analysis: English in Academic and Research Settings" (1990), Cambridge University Press
- Wang, X., "Topics over time: a non-Markov continuous-time model of topical trends", Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.424~433 (2006)
- Wei, X., "LDA-based document models for ad-hoc retrieval", Proceedings of the 29th annual international ACM SIGIR conference on Research and Development in Information Retrieval, pp.178~185 (2006)
- Zipf, G. K., "Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology" (1949), Addison-Wesley Press