Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation

This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full- edged lexical ambiguity resolution should combine several information sources and techniques. 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 construct complete taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomybuilding is not limitedto structured dictionaries such as LDOCE.

• Type: Conference
• Year: 1997
• Where: ACL
• Authors: German Rigau, Jordi Atserias, Eneko Agirre
• Keywords: ACL 1997, ACL 2007, Genus Terms, Word Sense, Word Sense Resolution