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Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation

German Rigau·Jordi Atserias·Eneko Agirre
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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. 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.

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German Rigau, Jordi Atserias, Eneko Agirre. 1997-04-21. Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation. https://arxiv.org/abs/cmp-lg/9704007

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