BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network

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 resour...

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Veröffentlicht in:Artificial intelligence Jg. 193; S. 217 - 250
Hauptverfasser: Navigli, Roberto, Ponzetto, Simone Paolo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier B.V 01.12.2012
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Graph algorithms
Information retrieval. Graph
Knowledge acquisition
Semantic networks
Speech and sound recognition and synthesis. Linguistics
Theoretical computing
Word sense disambiguation
Semantic networks
Graph algorithms
Knowledge acquisition
Word sense disambiguation
Semantic relation
Multilingualism
Disambiguation
Knowledge representation
Social network
Coverage
In vitro
Information language
Lexicography
Automatic translation
Polysemy
Semantic network
Linguistics
ISSN:0004-3702, 1872-7921
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Zusammenfassung: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.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2012.07.001