Using Wikipedia for Automatic Word Sense Disambiguation

Proceedings of NAACL HLT 2007, pages 196–203, Rochester, NY, April 2007. c⃝2007 Association for Computational Linguistics Using Wikipedia for Automatic Word Sense Disambiguation Rada Mihalcea Department of Computer Science University of North Texas rada@cs.unt.edu Abstract This paper describes a method for generat- ing sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, we show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers. 1 Introduction Ambiguity is inherent to human language. In partic- ular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. For instance, the English noun plant can mean green plant or factory; similarly the French word feuille can mean leaf or paper. The correct sense of an am- biguous word can be selected based on the context where it occurs, and correspondingly the problem of word sense disambiguation is defined as the task of automatically assigning the most appropriate mean- ing to a polysemous word within a given context. Among the various knowledge-based (Lesk, 1986; Galley and McKeown, 2003; Navigli and Ve- lardi, 2005) and data-driven (Yarowsky, 1995; Ng and Lee, 1996; Pedersen, 2001) word sense dis- ambiguation methods that have been proposed to date, supervised systems have been constantly ob- served as leading to the highest performance. In these systems, the sense disambiguation problem is formulated as a supervised learning task, where each sense-tagged occurrence of a particular word is transformed into a feature vector which is then used in an automatic learning process. Despite their high performance, these supervised systems have an important drawback: their applicability is limited to those few words for which sense tagged data is avail- able, and their accuracy is strongly connected to the amount of labeled data available at hand. To address the sense-tagged data bottleneck prob- lem, different methods have been proposed in the past, with various degrees of success. This includes the automatic generation of sense-tagged data using monosemous relatives (Leacock et al., 1998; Mi- halcea and Moldovan, 1999; Agirre and Martinez, 2004), automatically bootstrapped disambiguation patterns (Yarowsky, 1995; Mihalcea, 2002), paral- lel texts as a way to point out word senses bear- ing different translations in a second language (Diab and Resnik, 2002; Ng et al., 2003; Diab, 2004), and the use of volunteer contributions over the Web (Chklovski and Mihalcea, 2002). In this paper, we investigate a new approach for building sense tagged corpora using Wikipedia as a source of sense annotations. Starting with the hy- perlinks available in Wikipedia, we show how we can generate sense annotated corpora that can be used for building accurate and robust sense clas- sifiers. Through word sense disambiguation ex- periments performed on the Wikipedia-based sense tagged corpus generated for a subset of the SENSE- VAL ambiguous words, we show that the Wikipedia annotations are reliable, and the quality of a sense tagging classifier built on this data set exceeds by …

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