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WordNet is extensively used as a major lexical resource in NLP. However, its quality is far from perfect, and this alters the results of applications using it. We propose here to complement previous efforts for “cleaning up” the top level of its taxonomy with semi-automatic methods based on the detection of errors at the lower levels. The methods we propose test the coherence of two sources of knowledge, exploiting ontological principles and semantic constraints.
Nervo Verdezoto and Laure Vieu. ”Towards semi-automatic methods for improving WordNet”. In J.Bos and S. Pulman, editors, Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011) - Oxford, UK, January 2011, 275-284.