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).
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