Using parallel corpora for word sense disambiguation

Word Sense Disambiguation (WSD) is the Natural Language Processing (NLP) task that consists in selecting the correct sense of a polysemous word in a given context. Most state-of-the-art WSD systems are supervised classifiers that are trained on manually sense-tagged corpora, which are very time-consuming and expensive to build. In order to overcome this acquisition bottleneck (sense-tagged corpora are scarce for languages other than English), we decided to take a multilingual approach to WSD, that builds up the sense inventory on the basis of the Europarl parallel corpus [3]. Using translations from a parallel corpus implicitly deals with the granularity problem as finer sense distinctions are only relevant as far as they are lexicalized in the target translations. It also facilitates the integration of WSD in multilingual applications such as multilingual Information Retrieval (IR) or Machine Translation (MT).