BabelNet: Building a Very Large Multilingual Semantic Network

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 216–225, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics BabelNet: Building a Very Large Multilingual Semantic Network Roberto Navigli Dipartimento di Informatica Sapienza Universit`a di Roma navigli@di.uniroma1.it Simone Paolo Ponzetto Department of Computational Linguistics Heidelberg University ponzetto@cl.uni-heidelberg.de Abstract In this paper we present BabelNet – a very large, wide-coverage multilingual se- mantic network. The resource is automat- ically constructed by means of a method- ology that integrates lexicographic and en- cyclopedic knowledge from WordNet and Wikipedia. In addition Machine Transla- tion is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and ex- isting gold-standard datasets to show the high quality and coverage of the resource. 1 Introduction In many research areas of Natural Language Pro- cessing (NLP) lexical knowledge is exploited to perform tasks effectively. These include, among others, text summarization (Nastase, 2008), Named Entity Recognition (Bunescu and Pas¸ca, 2006), Question Answering (Harabagiu et al., 2000) and text categorization (Gabrilovich and Markovitch, 2006). Recent studies in the diffi- cult task of Word Sense Disambiguation (Nav- igli, 2009b, WSD) have shown the impact of the amount and quality of lexical knowledge (Cuadros and Rigau, 2006): richer knowledge sources can be of great benefit to both knowledge-lean systems (Navigli and Lapata, 2010) and supervised classi- fiers (Ng and Lee, 1996; Yarowsky and Florian, 2002). Various projects have been undertaken to make lexical knowledge available in a machine read- able format. A pioneering endeavor was Word- Net (Fellbaum, 1998), a computational lexicon of English based on psycholinguistic theories. Sub- sequent projects have also tackled the significant problem of multilinguality. These include Eu- roWordNet (Vossen, 1998), MultiWordNet (Pianta et al., 2002), the Multilingual Central Repository (Atserias et al., 2004), and many others. How- ever, manual construction methods inherently suf- fer from a number of drawbacks. First, maintain- ing and updating lexical knowledge resources is expensive and time-consuming. Second, such re- sources are typically lexicographic, and thus con- tain mainly concepts and only a few named enti- ties. Third, resources for non-English languages often have a much poorer coverage since the con- struction effort must be repeated for every lan- guage of interest. As a result, an obvious bias ex- ists towards conducting research in resource-rich languages, such as English. aA solution to these issues is to draw upon large-scale collaborative resource, namely Wikipedia1. Wikipedia represents the perfect com- plement to WordNet, as it provides multilingual lexical knowledge of a mostly encyclopedic na- ture. While the contribution of any individual user might be imprecise or inaccurate, the continual in- tervention of expert contributors in all domains re- sults in a resource of the highest quality (Giles, 2005). But while a great deal of work has been re- cently devoted to the automatic extraction of struc- tured information from Wikipedia (Wu and Weld, 2007; Ponzetto and Strube, 2007; Suchanek et al., 2008; Medelyan et al., 2009, inter alia), the knowledge extracted is …

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