Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the effectiveness of such an approach by attempting to disambiguate English texts through their translations in Italian, Romanian and Japanese. Specifically, we try to find the appropriate word senses for the English words by comparison with all the word senses associated to their translations. The main advantage of this approach is in that it can be applied to any parallel corpus, as long as large, high-quality inter-linked sense inventories exist for all the languages considered.

The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia.humnet.unipi.it/lexit. LexIt is the first large-scale resource for Italian in which subcategorization and semantic selection properties are characterized fully on distributional ground: in the paper we describe both the process of data extraction and the evaluation of the subcategorization frames extracted with LexIt.