Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The grammar of sense: Using part-of-speech tags as a first step in semantic disambiguation
Natural Language Engineering
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Verb class disambiguation using informative priors
Computational Linguistics
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Classifier optimization and combination in the English all words task
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Direct word sense matching for lexical substitution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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In this paper we show that an unsupervised method for ranking word senses automatically can be used to identify infrequently occurring senses. We demonstrate this using a ranking of noun senses derived from the BNC and evaluating on the sense-tagged text available in both SemCor and the SENSEVAL-2 English all-words task. We show that the method does well at identifying senses that do not occur in a corpus, and that those that are erroneously filtered but do occur typically have a lower frequency than the other senses. This method should be useful for word sense disambiguation systems, allowing effort to be concentrated on more frequent senses; it may also be useful for other tasks such as lexical acquisition. Whilst the results on balanced corpora are promising, our chief motivation for the method is for application to domain specific text. For text within a particular domain many senses from a generic inventory will be rare, and possibly redundant. Since a large domain specific corpus of sense annotated data is not available, we evaluate our method on domain-specific corpora and demonstrate that sense types identified for removal are predominantly senses from outside the domain.