Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
<|reference_start|>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-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.<|reference_end|>
@article{rigau1997combining,
    title={Combining Unsupervised Lexical Knowledge Methods for Word Sense
  Disambiguation},
    author={German Rigau, Jordi Atserias and Eneko Agirre},
    journal={Proceedings of ACL'97},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9704007},
    primaryClass={cmp-lg cs.CL}
}