A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

In: Proceedings of CoNLL-2000 and LLL-2000, pages 31-36, Lisbon, Portugal, 2000. A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation* Gerard Escudero and Lluis Mhrquez and German Rigau TALP Research Center. LSI Department. Universitat Polit~cnica de Catalunya (UPC) Jordi Girona Salgado 1-3. E-08034 Barcelona. Catalonia {escudero, lluism, g.rigau}@Isi.upc.es Abstract This paper describes a set of comparative exper- iments, including cross-corpus evaluation, be- tween five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNOW, Decision Lists, and Boosting. Two main conclu- sions can be drawn: 1) The LazyBoosting algo- rithm outperforms the other four state-of-the- art algorithms in terms of accuracy and ability to tune to new domains; 2) The domain depen- dence of WSD systems seems very strong and suggests that some kind of adaptation or tun- ing is required for cross-corpus application. 1 Introduction Word Sense Disambiguation (WSD) is the prob- lem of assigning the appropriate meaning (or sense) to a given word in a text or discourse. Resolving the ambiguity of words is a central problem for large scale language understanding applications and their associate tasks (Ide and V4ronis, 1998). Besides, WSD is one of the most important open problems in NLP. Despite the wide range of approaches investigated (Kilgar- rift and Rosenzweig, 2000) and the large effort devoted to tackling this problem, to date, no large-scale broad-coverage and highly accurate WSD system has been built. One of the most successful current lines of research is the corpus-based approach, in which statistical or Machine Learning (M L) algorithms have been applied to learn statistical models or classifiers from corpora in order to per- * This research has been partially funded by the Spanish Research Department (CICYT's project TIC98-0423- C06), by the EU Commission (NAMIC I8T-1999-12392), and by the Catalan Research Department (CIRIT's consolidated research group 1999SGR-150 and CIRIT's grant 1999FI 00773). form WSD. Generally, supervised approaches (those that learn from previously semantically annotated corpora) have obtained better results than unsupervised methods on small sets of se- lected ambiguous words, or artificial pseudo- words. Many standard M L algorithms for su- pervised learning have been applied, such as: Decision Lists (Yarowsky, 1994; Agirre and Martinez, 2000), Neural Networks (Towell and Voorhees, 1998), Bayesian learning (Bruce and Wiebe, 1999), Exemplar-based learning (Ng, 1997), Boosting (Escudero et al., 2000a), etc. Further, in (Mooney, 1996) some of the previ- ous methods are compared jointly with Decision Trees and Rule Induction algorithms, on a very restricted domain. Although some published works include the comparison between some alternative algo- rithms (Mooney, 1996; Ng, 1997; Escudero et al., 2000a; Escudero et al., 2000b), none of them addresses the issue of the portability of supervised ML algorithms for WSD, i.e., testing whether the accuracy of a system trained on a certain corpus can be extrapolated to other corpora or not. We think that the study of the domain dependence of WSD --in the style of other studies devoted to parsing (Sekine, 1997; Ratnaparkhi, 1999)-- is needed to assure the validity …

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