Bilingual Terminology Mining - Using Brain, not brawn comparable corpora

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 664–671, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Bilingual Terminology Mining – Using Brain, not brawn comparable corpora E. Morin, B. Daille Université de Nantes LINA FRE CNRS 2729 2, rue de la Houssinière BP 92208 F-44322 Nantes Cedex 03 {morin-e,daille-b}@ univ-nantes.fr K. Takeuchi Okayama University 3-1-1, Tsushimanaka Okayama-shi, Okayama, 700-8530, Japan koichi@ cl.it.okayama-u.ac.jp K. Kageura Graduate School of Education The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan kyo@p.u-tokyo.ac.jp Abstract Current research in text mining favours the quantity of texts over their quality. But for bilingual terminology mining, and for many language pairs, large comparable corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the quality rather than the quantity of the corpus matters more in terminology mining. Our hypothesis, therefore, is that the quality of the corpus is more important than the quan- tity and ensures the quality of the acquired terminological resources. We show how im- portant the type of discourse is as a charac- teristic of the comparable corpus. 1 Introduction Two main approaches exist for compiling corpora: “Big is beautiful” or “Insecurity in large collec- tions”. Text mining research commonly adopts the first approach and favors data quantity over qual- ity. This is normally justified on the one hand by the need for large amounts of data in order to make use of statistic or stochastic methods (Manning and Schütze, 1999), and on the other by the lack of oper- ational methods to automatize the building of a cor- pus answering to selected criteria, such as domain, register, media, style or discourse. For lexical alignment from comparable corpora, good results on single words can be obtained from large corpora — several millions words — the accu- racy of proposed translation is about 80% for the top 10-20 candidates (Fung, 1998; Rapp, 1999; Chiao and Zweigenbaum, 2002). (Cao and Li, 2002) have achieved 91% accuracy for the top three candidates using the Web as a comparable corpus. But for spe- cific domains, and many pairs of languages, such huge corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the quality rather than the quantity of the corpus matters more in terminology mining. For terminology mining, there- fore, our hypothesis is that the quality of the corpora is more important than the quantity and that this en- sures the quality of the acquired terminological re- sources. Comparable corpora are “sets of texts in different languages, that are not translations of each other” (Bowker and Pearson, 2002, p. 93). The term com- parable is used to indicate that these texts share some characteristics or features: topic, period, me- dia, author, register (Biber, 1994), discourse... This corpus comparability is discussed by lexical align- ment researchers but never demonstrated: it is of- ten reduced to a specific …

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