Abstract
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources.
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Notes
Definite clauses are first-order clauses containing one positive literal.
Horn clauses are first-order clauses that can contain at most one positive literal. A Horn clause with exactly one positive literal is a definite clause.
Where \(\wedge\) represents logical and, ⊧ logically proves and □ falsity.
A clause is satisfiable if there exists at least one model for it, i.e., there exists one interpretation (a set of ground facts) that assigns a true value for such clause.
See Sect. 4.1 for a discussion of OntoNote’s treatment of phrasal verbs such as “come back”.
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Acknowledgments
We are grateful for the feedback provided by the anonymous reviewers of this paper. Mark Stevenson was supported by the UK Engineering and Physical Sciences Research Council (grants EP/E004350/1 and EP/D069548/1).
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Specia, L., Stevenson, M. & das Graças Volpe Nunes, M. Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation. Lang Resources & Evaluation 44, 295–313 (2010). https://doi.org/10.1007/s10579-009-9107-y
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DOI: https://doi.org/10.1007/s10579-009-9107-y