Investigating Problems of Semi-supervised Learning for Word Sense Disambiguation

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 solutions for these problems, we generate some variants of bootstrapping algorithms and apply to word sense disambiguation. The experiments were done on the datasets of four words: interest, line, hard, and serve; and on English lexical sample of Senseval-3.