Paper: Improving Word Sense Disambiguation Using Topic Features

ACL ID D07-1108
Title Improving Word Sense Disambiguation Using Topic Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007

This paper presents a novel approach for ex- ploiting the global context for the task of word sense disambiguation (WSD). This is done by using topic features constructed us- ing the latent dirichlet allocation (LDA) al- gorithm on unlabeled data. The features are incorporated into a modified na¨ıve Bayes network alongside other features such as part-of-speech of neighboring words, single words in the surrounding context, local col- locations, and syntactic patterns. In both the English all-words task and the English lex- ical sample task, the method achieved sig- nificant improvement over the simple na¨ıve Bayes classifier and higher accuracy than the best official scores on Senseval-3 for both task.