Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1522–1531, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Knowledge-rich Word Sense Disambiguation Rivaling Supervised Systems Simone Paolo Ponzetto Department of Computational Linguistics Heidelberg University ponzetto@cl.uni-heidelberg.de Roberto Navigli Dipartimento di Informatica Sapienza Universit`a di Roma navigli@di.uniroma1.it Abstract One of the main obstacles to high- performance Word Sense Disambigua- tion (WSD) is the knowledge acquisi- tion bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of seman- tic relations from an encyclopedic re- source, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, sim- ple knowledge-lean disambiguation algo- rithms compete with state-of-the-art su- pervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets. 1 Introduction Knowledge lies at the core of Word Sense Dis- ambiguation (WSD), the task of computation- ally identifying the meanings of words in context (Navigli, 2009b). In the recent years, two main approaches have been studied that rely on a fixed sense inventory, i.e., supervised and knowledge- based methods. In order to achieve high perfor- mance, supervised approaches require large train- ing sets where instances (target words in con- text) are hand-annotated with the most appropri- ate word senses. Producing this kind of knowl- edge is extremely costly: at a throughput of one sense annotation per minute (Edmonds, 2000) and tagging one thousand examples per word, dozens of person-years would be required for en- abling a supervised classifier to disambiguate all the words in the English lexicon with high accu- racy. In contrast, knowledge-based approaches ex- ploit the information contained in wide-coverage lexical resources, such as WordNet (Fellbaum, 1998). However, it has been demonstrated that the amount of lexical and semantic information contained in such resources is typically insuffi- cient for high-performance WSD (Cuadros and Rigau, 2006). Several methods have been pro- posed to automatically extend existing resources (cf. Section 2) and it has been shown that highly- interconnected semantic networks have a great im- pact on WSD (Navigli and Lapata, 2010). How- ever, to date, the real potential of knowledge-rich WSD systems has been shown only in the presence of either a large manually-developed extension of WordNet (Navigli and Velardi, 2005) or sophisti- cated WSD algorithms (Agirre et al., 2009). The contributions of this paper are two-fold. First, we relieve the knowledge acquisition bot- tleneck by developing a methodology to extend WordNet with millions of semantic relations. The relations are harvested from an encyclopedic re- source, namely Wikipedia. Wikipedia pages are automatically associated with WordNet senses, and topical, semantic associative relations from Wikipedia are transferred to WordNet, thus pro- ducing a much richer lexical resource. Sec- ond, two simple knowledge-based algorithms that exploit our extended WordNet are applied to standard WSD datasets. The results show that the integration of vast amounts of semantic re- lations in knowledge-based systems yields per- formance competitive with state-of-the-art super- vised approaches on open-text WSD. In addition, we support …

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