Learning Semantic Classes for Word Sense Disambiguation

Proceedings of the 43rd Annual Meeting of the ACL, pages 34–41, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Learning Semantic Classes for Word Sense Disambiguation Upali S. Kohomban Wee Sun Lee Department of Computer Science National University of Singapore Singapore, 117584 {upalisat,leews}@comp.nus.edu.sg Abstract Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition bottleneck. Although state of the art supervised systems report good accu- racies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an ap- proach for learning coarser and more gen- eral set of concepts from a sense tagged corpus, in order to alleviate the knowl- edge acquisition bottleneck. We show that these general concepts can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that our approach can yield state of the art perfor- mance. 1 Introduction Word Sense Disambiguation (WSD) is the task of determining the meaning of a word in a given con- text. This task has a long history in natural language processing, and is considered to be an intermediate task, success of which is considered to be important for other tasks such as Machine Translation, Lan- guage Understanding, and Information Retrieval. Despite a long history of attempts to solve WSD problem by empirical means, there is not any clear consensus on what it takes to build a high perfor- mance implementation of WSD. Algorithms based on Supervised Learning, in general, show better per- formance compared to unsupervised systems. But they suffer from a serious drawback: the difficulty of acquiring considerable amounts of training data, also known as knowledge acquisition bottleneck. In the typical setting, supervised learning needs train- ing data created for each and every polysemous word; Ng (1997) estimates an effort of 16 person- years for acquiring training data for 3,200 significant words in English. Mihalcea and Chklovski (2003) provide a similar estimate of an 80 person-year ef- fort for creating manually labelled training data for about 20,000 words in a common English dictionary. Two basic approaches have been tried as solu- tions to the lack of training data, namely unsu- pervised systems and semi-supervised bootstrapping techniques. Unsupervised systems mostly work on knowledge-based techniques, exploiting sense knowledge encoded in machine-readable dictionary entries, taxonomical hierarchies such as WORD- NET (Fellbaum, 1998), and so on. Most of the bootstrapping techniques start from a few ‘seed’ la- belled examples, classify some unlabelled instances using this knowledge, and iteratively expand their knowledge using information available within newly labelled data. Some others employ hierarchical rel- atives such as hypernyms and hyponyms. In this work, we present another practical alterna- tive: we reduce the WSD problem to a one of finding generic semantic class of a given word instance. We show that learning such classes can help relieve the problem of knowledge acquisition bottleneck. 1.1 Learning senses as concepts As the semantic classes we propose learning, we use WORDNET lexicographer file identifiers …

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