USP-IBM-1 and USP-IBM-2: The ILP-based Systems for Lexical Sample WSD in SemEval-2007

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 442–445, Prague, June 2007. c⃝2007 Association for Computational Linguistics USP-IBM-1 and USP-IBM-2: The ILP-based Systems for Lexical Sample WSD in SemEval-2007 Lucia Specia, Maria das Grac¸as Volpe Nunes ICMC - University of S˜ao Paulo Trabalhador S˜ao-Carlense, 400, S˜ao Carlos, 13560-970, Brazil {lspecia, gracan}@icmc.usp.br Ashwin Srinivasan, Ganesh Ramakrishnan IBM India Research Laboratory Block 1, Indian Institute of Technology, New Delhi 110016, India {ashwin.srinivasan, ganramkr}@in.ibm.com Abstract We describe two systems participating of the English Lexical Sample task in SemEval- 2007. The systems make use of Inductive Logic Programming for supervised learning in two different ways: (a) to build Word Sense Disambiguation (WSD) models from a rich set of background knowledge sources; and (b) to build interesting features from the same knowledge sources, which are then used by a standard model-builder for WSD, namely, Support Vector Machines. Both sys- tems achieved comparable accuracy (0.851 and 0.857), which outperforms considerably the most frequent sense baseline (0.787). 1 Introduction Word Sense Disambiguation (WSD) aims to iden- tify the correct sense of ambiguous words in context. Results from the last edition of the Senseval com- petition (Mihalcea et al., 2004) have shown that, for supervised learning, the best accuracies are obtained with a combination of various types of features, to- gether with traditional machine learning algorithms based on feature-value vectors, such as Support Vec- tor Machines (SVMs) and Naive Bayes. While the features employed by these approaches are mostly considered to be “shallow”, that is, extracted from corpus or provided by shallow syntactic tools like part-of-speech taggers, it is generally thought that significant progress in automatic WSD would re- quire a “deep” approach in which access to substan- tial body of linguistic and world knowledge could assist in resolving ambiguities. Although the ac- cess to large amounts of knowledge is now possi- ble due to the availability of lexicons like WordNet, parsers, etc., the incorporation of such knowledge has been hampered by the limitations of the mod- elling techniques usually employed for WSD. Using certain sources of information, mainly relational in- formation, is beyond the capabilities of such tech- niques, which are based on feature-value vectors. Arguably, Inductive Logic Programming (ILP) sys- tems provide an appropriate framework for dealing with such data: they make explicit provisions for the inclusion of background knowledge of any form; the richer representation language used, based on first- order logic, is powerful enough to capture contextual relationships; and the modelling is not restricted to being of a particular form (e.g., classification). We describe the investigation of the use of ILP for WSD in the Lexical Sample task of SemEval- 2007 in two different ways: (a) the construction of models that can be used directly to disambiguate words; and (b) the construction of interesting fea- tures to be used by a standard feature-based algo- rithm, namely, SVMs, to build disambiguation mod- els. We call the systems resulting of the two differ- ent approaches “USP-IBM-1” and “USP-IBM-2”, respectively. The background knowledge is …

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