Automating the Acquisition of Bilingual Terminology

Automating the Acquisition of Bilingual Terminology Pim van der Eijk Digital Equipment Corporation Kabelweg 21 1014 BA Amsterdam The Netherlands eijk~cecehv.enet.dec.com Abstract As the acquisition problem of bilingual lists of terminological expressions is formidable, it is worthwhile to investigate methods to compile such lists as automatically as pos- sible. In this paper we discuss experimen- tal results for a number of methods, which operate on corpora of previously translated texts. Keywords: parallel corpora, tagging, ter- minology acquisition. 1 Introduction In the past several years, many researchers have started looking at bilingual corpora, as they im- plicitly contain much information needed for vari- ous purposes that would otherwise have to be com- piled manually. Some applications using information extracted from bilingual corpora are statistical MT ([Brown et al., 1990]), bilingual lexicography ([Cati- zone el al., 1989]), word sense disambiguation ([Gale et al., 1992]), and multilingual information retrieval ([Landauer and Littmann, 1990]). The goal of the research discussed in this paper is to automate as much as possible the generation of bilingual term lists from previously translated texts. These lists are used by terminologists and transla- tors, e.g. in documentation departments. Manual compilation of bilingual term lists is an expensive and laborious effort, hence the relative rarity of spe- cialized, up-to-date, and manageable terminological data collections. However, organizations interested in terminology and translation are likely to have archives of previously translated documents, which represent a considerable investment. Automatic or semi-automatic extraction of the information con- tained in these documents would then be an attrac- tive perspective. A bilingual term list is a list associating source language terms with a ranked list of target language terms. The methods to extract bilingual terminol- ogy from parallel texts were developed and evaluated experimentally using a bilingual, Dutch-English cor- pus. There are two phases in the process: 1. Process the texts to extract terms. The defini- tion of the notion 'term' will be an important issue of this paper, as it is necessary to adopt a definition that facilitates comparison of terms in the source and target language. Section 4 will show some flaws of methods that define terms as words or nouns. Terminologists commonly use full noun phrases 1 as terms to express (domain- specific) concepts. The NP level is shown to be a better level to compare Dutch and English in sections 5.1 and 5.2. This phase acts as a linguistic front end to the second phase. The various techniques used to process the corpus are described in section 2. 2. Apply statistic techniques to determine corres- pondences between source and target language. In section 3 we will introduce a simple algorithm to select and order potential translations for a given term. This method will subsequently be compared to two other methods discussed in the literature. The usual benefits of modularity apply because the two phases are highly independent. 1To some extent, a particular domain will also have textual elements specific to the domain that are not NPs. We will ignore these, but essentially …

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