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License. BabelNet is both a multilingual encyclopedic dictionary, with lexicographic and encyclopedic coverage of terms in 500 languages, and a semantic network which connects concepts and named entities in a very large network of semantic relations, made up of more than 20 million entries.
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About. BabelNet is an innovative multilingual encyclopedic...
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Multilingual Word Sense Disambiguation datasets annotated...
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BabelNet is both a multilingual encyclopedic dictionary,...
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BabelNet Non-Commercial license: This is a human-readable...
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BabelNet is both a multilingual encyclopedic dictionary,...
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BabelNet is a multilingual semantic network obtained as an integration of WordNet and Wikipedia. Statistics of BabelNet. As of April 2021, BabelNet (version 5.0) covers 500 languages. It contains almost 20 million synsets and around 1.4 billion word senses (regardless of their language).
- BabelNet 5.0, / February 2021
5 days ago · BabelNet: Building a Very Large Multilingual Semantic Network - ACL Anthology. Anthology ID: P10-1023. Volume: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Month: July. Year: 2010. Address: Uppsala, Sweden. Editors: Jan Hajič , Sandra Carberry , Stephen Clark , Joakim Nivre. Venue: ACL. SIG: Publisher:
- Roberto Navigli, Simone Paolo Ponzetto
- 2010
Jan 1, 2013 · BabelNet is a large semantic network that integrates lexicographic information from WordNet with encyclopedic knowledge from Wikipedia and machine translation. It provides an API for accessing lexical and semantic information for six languages: Catalan, English, French, German, Italian and Spanish.
- Roberto Navigli, Simone Paolo Ponzetto
- 2013
Sep 24, 2010 · BabelNet: Building a Very Large Multilingual Semantic Network. September 2010. Source. DBLP. Conference: ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational...
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Roberto Navigli. 2012. Abstract 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.