USP-IBM-1 and USP-IBM-2: the ILP-based systems for lexical sample WSD in SemEval-2007

Authors:
Lucia Specia;Maria das Graças;Volpe Nunes;Ashwin Srinivasan;Ganesh Ramakrishnan
Affiliations:
University of São Paulo, São Carlos, Brazil;University of São Paulo, São Carlos, Brazil;University of São Paulo, São Carlos, Brazil;IBM India Research Laboratory, New Delhi, India;IBM India Research Laboratory, New Delhi, India
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

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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 systems achieved comparable accuracy (0.851 and 0.857), which outperforms considerably the most frequent sense baseline (0.787).