BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network
|BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network|
|Author(s)||Navigli R., Ponzetto S.P.|
|Published in||Artificial Intelligence|
|Keyword(s)||Graph algorithms, Knowledge acquisition, Semantic networks, Word sense disambiguation (Extra: Automatic construction, Cross-lingual, Data sets, Encyclopedic knowledge, Graph algorithms, High quality, In-vitro, Lexical coverage, Lexical information, Lexical resources, Machine translations, Semantic network, Semantic relations, Wikipedia, Word Sense Disambiguation, Wordnet, Knowledge acquisition, Semantics, Natural language processing systems)|
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BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network is a 2012 journal article written in English by Navigli R., Ponzetto S.P. and published in Artificial Intelligence.
We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks. © 2012 Elsevier B.V.
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