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Finding predominant word senses in untagged text
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Supersense tagging of unknown nouns using semantic similarity
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Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Predicting part-of-speech tags and morpho-syntactic relations using similarity-based technique
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We present a new framework for classifying common nouns that extends named-entity classification. We used a fixed set of 26 semantic labels, which we called supersenses. These are the labels used by lexicographers developing WordNet. This framework has a number of practical advantages. We show how information contained in the dictionary can be used as additional training data that improves accuracy in learning new nouns. We also define a more realistic evaluation procedure than cross-validation.