Measuring and Improving the Quality of World Knowledge extracted from WordNet
Measuring and Improving the Quality of World Knowledge extracted from WordNet
AI Magazine
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Automatic Discovery of Part-Whole Relations
Computational Linguistics
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
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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.