Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 415–422, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora Zheng-Yu Niu, Dong-Hong Ji Institute for Infocomm Research 21 Heng Mui Keng Terrace 119613 Singapore {zniu, dhji}@i2r.a-star.edu.sg Chew Lim Tan Department of Computer Science National University of Singapore 3 Science Drive 2 117543 Singapore tancl@comp.nus.edu.sg Abstract Supervised and semi-supervised sense dis- ambiguation methods will mis-tag the in- stances of a target word if the senses of these instances are not defined in sense in- ventories or there are no tagged instances for these senses in training data. Here we used a model order identification method to avoid the misclassification of the in- stances with undefined senses by discov- ering new senses from mixed data (tagged and untagged corpora). This algorithm tries to obtain a natural partition of the mixed data by maximizing a stability cri- terion defined on the classification result from an extended label propagation al- gorithm over all the possible values of the number of senses (or sense number, model order). Experimental results on SENSEVAL-3 data indicate that it outper- forms SVM, a one-class partially super- vised classification algorithm, and a clus- tering based model order identification al- gorithm when the tagged data is incom- plete. 1 Introduction In this paper, we address the problem of partially supervised word sense disambiguation, which is to disambiguate the senses of occurrences of a tar- get word in untagged texts when given incomplete tagged corpus 1. Word sense disambiguation can be defined as associating a target word in a text or discourse 1“incomplete tagged corpus” means that tagged corpus does not include the instances of some senses for the target word, while these senses may occur in untagged texts. with a definition or meaning. Many corpus based methods have been proposed to deal with the sense disambiguation problem when given definition for each possible sense of a target word or a tagged corpus with the instances of each possible sense, e.g., supervised sense disambiguation (Leacock et al., 1998), and semi-supervised sense disambigua- tion (Yarowsky, 1995). Supervised methods usually rely on the infor- mation from previously sense tagged corpora to determine the senses of words in unseen texts. Semi-supervised methods for WSD are charac- terized in terms of exploiting unlabeled data in the learning procedure with the need of prede- fined sense inventories for target words. The in- formation for semi-supervised sense disambigua- tion is usually obtained from bilingual corpora (e.g. parallel corpora or untagged monolingual corpora in two languages) (Brown et al., 1991; Da- gan and Itai, 1994), or sense-tagged seed examples (Yarowsky, 1995). Some observations can be made on the previous supervised and semi-supervised methods. They always rely on hand-crafted lexicons (e.g., Word- Net) as sense inventories. But these resources may miss domain-specific senses, which leads to in- complete sense tagged corpus. Therefore, sense taggers trained on the incomplete tagged corpus will misclassify …

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