Combining Weak Knowledge Sources for Sense Disambiguation

There has been a tradition of combining different knowledge sources in Artificial Intelligence research. We apply this methodology to word sense disambiguation (WSD), a long-standing problem in Computational Linguistics. We report on an implemented sense tagger which uses a machine readable dictionary to provide both a set of senses and associated forms of information on which to base disambiguation decisions. The system is based on an architecture which makes use of different sources of lexical knowledge in two ways and optimises their combination using a learning algorithm. Tested accuracy of our approach on a general corpus exceeds 94%, demonstrating the viability of allword disambiguation as opposed to restricting oneself to a small sample. 1 Introduction The methodology and evaluation of word sense disambiguation (WSD) as a distinct task are somewhat different from those of others in NLP, and one can distinguish three aspects of this difference, all of which come down to evaluat..