  Word sense disambiguation algorithms, with few exceptions, have made use of
only one lexical knowledge source. We describe a system which performs
unrestricted 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. The
usefulness of these sources is 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.
