Personalizing PageRank for Word Sense Disambiguation

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 33–41, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Personalizing PageRank for Word Sense Disambiguation Eneko Agirre and Aitor Soroa IXA NLP Group University of the Basque Country Donostia, Basque Contry {e.agirre,a.soroa}@ehu.es Abstract In this paper we propose a new graph- based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disam- biguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other lan- guages with good results, with the only re- quirement of having a wordnet. In addi- tion, we make an analysis of the perfor- mance of the algorithm, showing that it is efficient and that it could be tuned to be faster. 1 Introduction Word Sense Disambiguation (WSD) is a key enabling-technology that automatically chooses the intended sense of a word in context. Super- vised WSD systems are the best performing in public evaluations (Palmer et al., 2001; Snyder and Palmer, 2004; Pradhan et al., 2007) but they need large amounts of hand-tagged data, which is typically very expensive to build. Given the rela- tively small amount of training data available, cur- rent state-of-the-art systems only beat the simple most frequent sense (MFS) baseline1 by a small margin. As an alternative to supervised systems, knowledge-based WSD systems exploit the infor- mation present in a lexical knowledge base (LKB) to perform WSD, without using any further corpus evidence. 1This baseline consists of tagging all occurrences in the test data with the sense of the word that occurs more often in the training data Traditional knowledge-based WSD systems as- sign a sense to an ambiguous word by comparing each of its senses with those of the surrounding context. Typically, some semantic similarity met- ric is used for calculating the relatedness among senses (Lesk, 1986; McCarthy et al., 2004). One of the major drawbacks of these approaches stems from the fact that senses are compared in a pair- wise fashion and thus the number of computa- tions can grow exponentially with the number of words. Although alternatives like simulated an- nealing (Cowie et al., 1992) and conceptual den- sity (Agirre and Rigau, 1996) were tried, most of past knowledge based WSD was done in a subop- timal word-by-word process, i.e., disambiguating words one at a time. Recently, graph-based methods for knowledge- based WSD have gained much attention in the NLP community (Sinha and Mihalcea, 2007; Nav- igli and Lapata, 2007; Mihalcea, 2005; Agirre and Soroa, 2008). These methods use well-known graph-based techniques to find and exploit the structural properties of the graph underlying a par- ticular LKB. Because the graph is analyzed as a whole, these techniques have the remarkable prop- erty of being able to find globally optimal solu- tions, given the relations between entities. Graph- based WSD methods are particularly …

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