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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics on - NAACL '09
We supplement WordNet entries with information on the subjectivity of its word senses. Supervised classifiers that operate on word sense definitions in the same way that text classifiers operate on web or newspaper texts need large amounts of training data. The resulting data sparseness problem is aggravated by the fact that dictionary definitions are very short. We propose a semi-supervised minimum cut framework that makes use of both WordNet definitions and its relation structure. Thedoi:10.3115/1620754.1620756 fatcat:y6fpas3bdbgpjnwx53pbf3hdfu