Using WordNet and Semantic Similarity for Bilingual Terminology Mining from Comparable Corpora

This paper presents an extension of the standard approach used for bilingual lexicon extraction from comparable corpora. We study of the ambiguity problem revealed by the seed bilingual dictionary used to translate context vectors. For this purpose, we augment the standard approach by a Word Sense Disambiguation process relying on a WordNet-based semantic similarity measure. The aim of this process is to identify the translations that are more likely to give the best representation of words in the target language. On two specialized French-English comparable corpora, empirical experimental results show that the proposed method consistently outperforms the standard approach.

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