Cross-lingual WSD for Translation Extraction from Comparable Corpora

We propose a data-driven approach to enhance translation extraction from comparable corpora. Instead of resorting to an external dictionary, we translate source vector features by using a cross-lingual Word Sense Disambiguation method. The candidate senses for a feature correspond to sense clusters of its translations in a parallel corpus and the context used for disambiguation consists of the vector that contains the feature. The translations found in the disambiguation output convey the sense of the features in the source vector, while the use of translation clusters permits to expand their translation with several variants. As a consequence, the translated vectors are less noisy and richer, and allow for the extraction of higher quality lexicons compared to simpler methods.