Searching Parallel Corpora for Contextually Equivalent Terms

© 2011 European Association for Machine Translation Searching Parallel Corpora for Contextually Equivalent Terms Caroline Barrière Centre de Recherche Informatique de Montréal (CRIM) i Montréal, Québec, Canada caroline.barriere@crim.ca Pierre Isabelle Institute for Information Technology National Research Council Canada Gatineau, Québec, Canada pierre.isabelle@nrc-cnrc.gc.ca Abstract In this paper, we show how a large bilingual English-French parallel corpus can be brought to bear in terminology search. First, we dem- onstrate that the coverage of available corpora has become substantially more extensive than that of mainstream term banks. One potential drawback in searching large unstructured cor- pora is that large numbers of search results may need to be examined before finding a relevant match. We argue that this problem can be alleviated by contextualizing the search process: instead of looking up isolated terms one searches for terms appearing in a context that is similar to that of the term to be trans- lated. We present an experiment on context- based re-ranking and report highly positive re- sults. We conclude that translators will in- creasingly rely on very large scale corpora for searching term equivalents. 1. Introduction Isabelle (1992) has often been cited for the observation that bilingual corpora contain more solutions to more translation problems than any other existing resource. As a matter of fact, in recent years translators have been increasingly turning to corpus-based tools: translation memories, bilingual concordancers and, more recently, statistical machine transla- tion. Yet, it seems that for the specific task of look- ing up the translation of technical terms, trans- lators continue to prefer traditional term banks and corpora remain to this day at best a secon- dary resource. Term banks offer at least two important advan- tages: 1) their content is edited and validated by experts, so that translators feel they can mostly trust it; and 2) each database record is manually assigned domain categories that help users select contextually appropriate transla- tions for polysemous terms. But term banks also have some significant drawbacks. Most importantly: a) their development is extremely time-consuming and costly; and b) their cover- age is always incomplete, especially for re- cently-introduced (and thus lesser known) terms. In this paper, we first argue that corpus search can circumvent some of the drawbacks of tra- ditional term banks. Clearly, development time and cost are much less of an issue with corpora since they are mostly a product of the normal activity of translators. But until recently, no corpora were available whose terminological coverage would rival that of the mainstream general-purpose term banks. In this paper, we show that this is no longer true: the larger ex- isting corpora now cover substantially more technical terms than the larger existing term banks. Second, we argue that some of the known ad- vantages of term banks can be emulated in the world of corpora. Note, however, that we will not address the issue of trust in the present paper. We will focus mainly on the issue of knowledge organization and argue that even though corpora lack an explicit …

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