Class Based Sense Definition Model for Word Sense Tagging and Disambiguation

Abstract

We present an unsupervised learning strategy for word sense disambiguation (WSD) that exploits multiple linguistic resources including a parallel corpus, a bilingual machine readable dictionary, and a thesaurus. The approach is based on Class Based Sense Definition Model (CBSDM) that generates the glosses and translations for a class of word senses. The model can be applied to resolve sense ambiguity for words in a parallel corpus. That sense tagging procedure, in effect, produces a semantic bilingual concordance, which can be used to train WSD systems for the two languages involved. Experimental results show that CBSDM trained on Longman Dictionary of Contemporary English, English-Chinese Edition (LDOCE E-C) and Longman Lexicon of Contemporary English (LLOCE) is very effectively in turning a Chinese-English parallel corpus into sense tagged data for development of WSD systems.