Supervised Word Sense Disambiguation with Support Vector Machines and multiple knowledge sources

Supervised Word Sense Disambiguation with Support Vector Machines and Multiple Knowledge Sources Yoong Keok Lee and Hwee Tou Ng and Tee Kiah Chia Department of Computer Science National University of Singapore 3 Science Drive 2, Singapore 117543 y.k.lee@alumni.nus.edu.sg nght@comp.nus.edu.sg chiateek@comp.nus.edu.sg Abstract We participated in the SENSEVAL-3 English lexi- cal sample task and multilinguallexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data provided. No other external resources were used. The knowledge sources used were part- of-speech of neighboring words, single words in the surrounding context, local collocations, and syntac- tic relations. For the translation and sense subtask of the multilingual lexical sample task, the English sense given for the target word was also used as an additional knowledge source. For the English lexical sample task, we obtained fine-grained and coarse-grained score (for both recall and precision) of 0.724 and 0.788 respectively. For the multilin- gual lexical sample task, we obtained recall (and precision) of 0.634 for the translation subtask, and 0.673 for the translation and sense subtask. 1 Introduction This paper describes the approach adopted by our systems which participated in the English lexical sample task and the multilingual lexical sample task of SENSEVAL-3. The goal of the English lexical sample task is to predict the correct sense of an am- biguous English word � , while that of the multi- lingual lexical sample task is to predict the correct Hindi (target language) translation of an ambiguous English (source language) word � . The multilingual lexical sample task is further subdivided into two subtasks: the translation sub- task, as well as the translation and sense subtask. The distinction is that for the translation and sense subtask, the English sense of the target ambiguous word � is also provided (for both training and test data). In all, we submitted 3 systems: system nusels for the English lexical sample task, system nusmlst for the translation subtask, and system nusmlsts for the translation and sense subtask. All systems were based on the supervised word sense disambiguation (WSD) system of Lee and Ng (2002), and used Support Vector Machines (SVM) learning. Only the training examples provided in the official training corpus were used to train the sys- tems, and no other external resources were used. In particular, we did not use any external dictionary or the sample sentences in the provided dictionary. The knowledge sources used included part-of- speech (POS) of neighboring words, single words in the surrounding context, local collocations,and syn- tactic relations, as described in Lee and Ng (2002). For the translation and sense subtask of the multi- lingual lexical sample task, the English sense given for the target word was also used as an additional knowledge source. All features encoding these knowledge sources were used, without any feature selection. We next describe SVM learning and the com- bined knowledge sources adopted. Much of the de- scription follows that of Lee and Ng (2002). 2 Support Vector Machines (SVM) The SVM (Vapnik, 1995) …

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