BABELNET 3.5, Largest Multilingual Dictionary and Semantic Network
BabelNet 3.5 covers 272 languages, and offers an improved user interface, new integrated resources of Wikiquote, VerbNet, Microsoft Terminology, GeoNames, WoNeF and ImageNet, and a very large knowledge base with over 380 million semantic relations.
As an output of the "MultiJEDI" Starting Grant, funded by the European Research Council and headed by Prof. Roberto Navigli, the Linguistic Computing Laboratory of the Sapienza University of Rome is proud to announce the release of BabelNet 3.5.
BabelNet is the largest multilingual encyclopedic dictionary and semantic network created by means of the seamless integration of the largest multilingual Web encyclopedia - i.e., Wikipedia - with the most popular computational lexicon of English - i.e., WordNet, and other lexical semantic resources such as Wiktionary, OmegaWiki, Wikidata, OpenMultilingual WordNet, Wikiquote, VerbNet, Microsoft Terminology, GeoNames, WoNeF, and ImageNet. The integration is performed via a high-performance linking algorithm and by filling in lexical gaps with the aid of Machine Translation. The result is an encyclopedic dictionary that provides Babelsynsets, i.e., concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations.
Version 3.5 comes with the following new features:
- Improved user interface
- 272 languages now covered!
- New resources integrated: Wikiquote, VerbNet, Microsoft Terminology, GeoNames, WoNeF and ImageNet.
- 13.8M Babel synsets and 119M Babel senses
- 1.5M synsets associated with domains (e.g. here)
- 700K compounds linked to synsets (e.g. here and then click “Compounds” on the left menu)
- Now a very large knowledge base, with 380M semantic relations among which 58M are labeled, with an inventory of 2655 distinct relation types.
- Fully taxonomized thanks to the seamless integration of our multilingual Wikipedia Bitaxonomy
- 10M synset-associated images and 40.6M textual definitions
- A new, improved version of the Java and HTTP RESTful API
- High-performance word sense disambiguation and entity linking with Babelfy in any language (including a language-agnostic setting!)
- Explicit and latent vector representations for millions of concepts and named entities with NASARI and SensEmbed for state-of-the-art semantic similarity (see http://babelnet.org/papers)!
Dipartimento di Informatica
Sapienza University of Rome
Home Page: http://wwwusers.di.uniroma1.it/~navigli
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