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BabelNet: Building a Very Large Multilingual Semantic Network

Summary

The paper addresses the lack of large multilingual lexical-semantic resources. Manually compiled wordnets are expensive to maintain, contain few named entities, and offer weak coverage for most languages. The authors propose BabelNet, a semantic network that is built automatically. It combines lexicographic knowledge from WordNet 3.0 with encyclopedic knowledge from the English Wikipedia. First, Wikipedia pages are mapped to WordNet senses. To do this, sets of context words are collected on both sides and matches between them are sought. On the Wikipedia side, these are the disambiguation labels in page titles, links, and categories. On the WordNet side, they are synonyms, hypernyms, hyponyms, "sister" synsets, and glosses. Next, each concept receives a babel synset, i.e., the set of its names in different languages. These names are taken from Wikipedia's interlanguage links and redirects. Gaps are filled by machine translation of sentences from SemCor and Wikipedia in which the concept occurs. An evaluation on five languages showed that the coverage is many times greater than that of the national wordnets. Translation precision is high, especially where Wikipedia is involved. For resource-poor languages, this means broad coverage of concepts and entities together with semantic relations.

Key points

  • Sources: WordNet 3.0 and the English Wikipedia dump of November 3, 2009 (3,083,466 articles). The resource covers only nouns, since Wikipedia mainly describes nominal entities.
  • The structure is a graph G = (V, E). The vertices are concepts and named entities with names in several languages. The edges are typed relations from WordNet (is-a, part-of, etc.) and untyped relations derived from Wikipedia hyperlinks.
  • Evaluation languages: German, Spanish, Catalan, Italian, and French. Compared with the national wordnets, BabelNet provides between 340% and 2,298% additional coverage.
  • Manual validation of synonym precision yielded the following results: about 72% for WordNet-only synsets, about 80% for concepts shared by WordNet and Wikipedia, and about 95% for Wikipedia-only synsets.
  • The closest predecessor is Universal WordNet (de Melo & Weikum, 2009): about 800K words in over 200 languages. Unlike it, BabelNet merges the relational structure of WordNet and the semi-structured knowledge of Wikipedia into a single network.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Topics Multilingual & cross-lingual NLP

Cite

Cite as Roberto Navigli and Simone Paolo Ponzetto. 2010. BabelNet: Building a Very Large Multilingual Semantic Network. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{navigli2010babelnet,
  title = {{BabelNet: Building a Very Large Multilingual Semantic Network}},
  author = {Roberto Navigli and Simone Paolo Ponzetto},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/P10-1023/}
}

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