%0 Journal Article
%T Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
%A German Rigau
%A Jordi Atserias
%A Eneko Agirre
%J Computer Science
%D 1997
%I arXiv
%X 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. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomy building is not limited to structured dictionaries such as LDOCE.
%U http://arxiv.org/abs/cmp-lg/9704007v1