Combining Bilingual Lexicons Extracted from Comparable Corpora: The Complementary Approach Between Word Embedding and Text Mining

Recently, different works on bilingual lexicon extraction from comparable corpora have been proposed. This paper presents how to combine differents methods for bilingual lexicon extraction based on standard context vectors and advanced text mining methods. In this respect, we focus on combining bilingual lexicons based on context vectors, association rules and contextual meta-rules. The combination of lexicons leads to a less sparse representation in order to extract the most effective translations from these lexicons and create an optimal bilingual lexicon. An experimental validation conducted on two pairs of languages of the CLEF 2003 campaign evaluation, shows that the combination of the models give a significant improvement compared to the standard approach.