Language-Independent Bilingual Terminology Extraction from a Multilingual Parallel Corpus

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 496–504, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Language-independent bilingual terminology extraction from a multilingual parallel corpus Els Lefever1,2, Lieve Macken1,2 and Veronique Hoste1,2 1LT3 School of Translation Studies University College Ghent Groot-Brittanni¨elaan 45 9000 Gent, Belgium 2Department of Applied Mathematics and Computer Science Ghent University Krijgslaan281-S9 9000 Gent, Belgium {Els.Lefever, Lieve.Macken, Veronique.Hoste}@hogent.be Abstract We present a language-pair independent terminology extraction module that is based on a sub-sentential alignment sys- tem that links linguistically motivated phrases in parallel texts. Statistical filters are applied on the bilingual list of candi- date terms that is extracted from the align- ment output. We compare the performance of both the alignment and terminology extrac- tion module for three different language pairs (French-English, French-Italian and French-Dutch) and highlight language- pair specific problems (e.g. different com- pounding strategy in French and Dutch). Comparisons with standard terminology extraction programs show an improvement of up to 20% for bilingual terminology ex- traction and competitive results (85% to 90% accuracy) for monolingual terminol- ogy extraction, and reveal that the linguis- tically based alignment module is particu- larly well suited for the extraction of com- plex multiword terms. 1 Introduction Automatic Term Recognition (ATR) systems are usually categorized into two main families. On the one hand, the linguistically-based or rule-based approaches use linguistic information such as PoS tags, chunk information, etc. to filter out stop words and restrict candidate terms to predefined syntactic patterns (Ananiadou, 1994), (Dagan and Church, 1994). On the other hand, the statistical corpus-based approaches select n-gram sequences as candidate terms that are filtered by means of statistical measures. More recent ATR systems use hybrid approaches that combine both linguis- tic and statistical information (Frantzi and Anani- adou, 1999). Most bilingual terminology extraction systems first identify candidate terms in the source lan- guage based on predefined source patterns, and then select translation candidates for these terms in the target language (Kupiec, 1993). We present an alternative approach that gen- erates candidate terms directly from the aligned words and phrases in our parallel corpus. In a sec- ond step, we use frequency information of a gen- eral purpose corpus and the n-gram frequencies of the automotive corpus to determine the term specificity. Our approach is more flexible in the sense that we do not first generate candidate terms based on language-dependent predefined PoS pat- terns (e.g. for French, N N, N Prep N, and N Adj are typical patterns), but immediately link lin- guistically motivated phrases in our parallel cor- pus based on lexical correspondences and syntac- tic similarity. This article reports on the term extraction ex- periments for 3 language pairs, i.e. French-Dutch, French-English and French-Italian. The focus was on the extraction of automative lexicons. The remainder of this paper is organized as fol- lows: Section 2 describes the corpus. In Section 3 we present our linguistically-based sub-sentential alignment system and in Section 4 we describe how we …

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