Word Sense Disambiguation using Optimised Combinations of Knowledge Sources

Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs word sense disambiguation on all content words in free text by combining different knowledge sources: semantic preferences, dictionary definitions and subject /domain codes along with part-of-speech tags, optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample. 1 Introduction This paper describes a system that integrates a number of partial sources of information to perform word sense disambiguation (WSD) of content words in general text at a high level of accuracy. The methodology and evaluation of WSD are somewhat different from those of other NLP modules, and one can distinguish three aspects of ..