Combining unsupervised lexical knowledge methods for word sense disambiguation

Abstract

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-fledged 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. Texted accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that texonomy building is not limited to structured dictionaries such as LDOCE.