of the 2012 Conference on Empirical Methods in Natural Language Processing (EMNLP 2012), Jeju, Korea, July 12–14, 2012, pp. Joining Forces Pays Off: Multilingual Joint Word Sense Disambiguation. of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2015), Denver, Colorado (US), 31 May-5 June 2015, pp. NASARI: a Novel Approach to a Semantically-Aware Representation of Items. of the 26th AAAI Conference on Artificial Intelligence (AAAI 2012), Toronto, Canada, pp. BabelRelate! A Joint Multilingual Approach to Computing Semantic Relatedness. of the 9th Language Resources and Evaluation Conference (LREC 2014), Reykjavik, Iceland, 26–. Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), Uppsala, Sweden, July 11–16, 2010, pp. BabelNet: Building a Very Large Multilingual Semantic Network. Artificial Intelligence, 193, Elsevier, pp. BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. multilingual Word Sense Disambiguation and Entity Linking with the Babelfy system īabelNet received the META prize 2015 for 'groundbreaking work in overcoming language barriers through a multilingual lexicalised semantic network and ontology making use of heterogeneous data sources'.īabelNet featured prominently in a TIME magazine's article about the new age of innovative and up-to-date lexical knowledge resources available on the Web.multilingual Word Sense Disambiguation.The lexicalized knowledge available in BabelNet has been shown to obtain state-of-the-art results in: 2.67 million synsets are assigned domain labels.īabelNet has been shown to enable multilingual Natural Language Processing applications. Version 4.0 also associates about 53 million images with Babel synsets and provides a Lemon RDF encoding of the resource, available via a SPARQL endpoint. The semantic network includes all the lexico-semantic relations from WordNet ( hypernymy and hyponymy, meronymy and holonymy, antonymy and synonymy, etc., totaling around 364,000 relation edges) as well as an underspecified relatedness relation from Wikipedia (totaling around 1,3 bilion edges).

Each Babel synset contains 2 synonyms per language, i.e., word senses, on average. BabelNet 4.0 contains almost 16 million synsets and about 833 million word senses (regardless of their language). For each Babel synset, BabelNet provides short definitions (called glosses) in many languages harvested from both WordNet and Wikipedia.Īs of February 2018, BabelNet (version 4.0) covers 284 languages, including all European languages, most Asian languages, and Latin. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. The result is an 'encyclopedic dictionary' that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. Attribution-NonCommercial-ShareAlike 3.0 UnportedīabelNet is a multilingual lexicalized semantic network and ontology developed at the Sapienza University of Rome, at the Department of Computer Science Linguistic Computing Laboratory.

Babelnet as an app