Bilingual Lexicon Extraction from Comparable Corpora Based on Closed Concepts Mining

In this paper, we propose to complement the context vectors used in bilingual lexicon extraction from comparable corpora with concept vectors, that aim at capturing all the words related to the concepts associated with a given word. This allows one to rely on a representation that is less sparse, especially in specialized domains where the use of a general bilingual lexicon leaves many words untranslated. The concept vectors we are considering are based on closed concepts mining developed in Formal Concept Analysis (FCA). The obtained results on two different comparable corpora show that enriching context vectors with concept vectors leads to lexicons of higher quality, especially in specialized domains.