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Investigating Problems of Semi-supervised Learning for Word Sense Disambiguation
[chapter]
2006
Lecture Notes in Computer Science
Word Sense Disambiguation (WSD) is the problem of determining the right sense of a polysemous word in a given context. In this paper, we will investigate the use of unlabeled data for WSD within the framework of semi supervised learning, in which the original labeled dataset is iteratively extended by exploiting unlabeled data. This paper addresses two problems occurring in this approach: determining a subset of new labeled data at each extension and generating the final classifier. By giving
doi:10.1007/11940098_51
fatcat:zkdtmlz7tvcmfepfj6wwvukw3i