Paper: Combining Unsupervised Lexical Knowledge Methods For Word Sense Disambiguation

ACL ID P97-1007
Title Combining Unsupervised Lexical Knowledge Methods For Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997
Authors

This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Al- though most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should com- bine several information sources and tech- niques. The set of techniques have been applied in a combined way to disambiguate the genus terms of two machine-readable dictionaries (MRD), enabling us to con- struct complete taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomy build- ing is not limited to structured dictionaries such as LDOCE.