IIITH: Domain Specific Word Sense Disambiguation

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 387–391, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics IIITH: Domain Specific Word Sense Disambiguation Siva Reddy IIIT Hyderabad India gvsreddy@students.iiit.ac.in Diana McCarthy Lexical Computing Ltd. United Kingdom diana@dianamccarthy.co.uk Abhilash Inumella IIIT Hyderabad India abhilashi@students.iiit.ac.in Mark Stevenson University of Sheffield United Kingdom m.stevenson@dcs.shef.ac.uk Abstract We describe two systems that participated in SemEval-2010 task 17 (All-words Word Sense Disambiguation on a Specific Do- main) and were ranked in the third and fourth positions in the formal evaluation. Domain adaptation techniques using the background documents released in the task were used to assign ranking scores to the words and their senses. The test data was disambiguated using the Personalized PageRank algorithm which was applied to a graph constructed from the whole of WordNet in which nodes are initialized with ranking scores of words and their senses. In the competition, our systems achieved comparable accuracy of 53.4 and 52.2, which outperforms the most frequent sense baseline (50.5). 1 Introduction The senses in WordNet are ordered according to their frequency in a manually tagged corpus, Sem- Cor (Miller et al., 1993). Senses that do not oc- cur in SemCor are ordered arbitrarily after those senses of the word that have occurred. It is known from the results of SENSEVAL2 (Cotton et al., 2001) and SENSEVAL3 (Mihalcea and Edmonds, 2004) that first sense heuristic outperforms many WSD systems (see McCarthy et al. (2007)). The first sense baseline’s strong performance is due to the skewed frequency distribution of word senses. WordNet sense distributions based on SemCor are clearly useful, however in a given domain these distributions may not hold true. For example, the first sense for “bank” in WordNet refers to “slop- ing land beside a body of river” and the second to “financial institution”, but in the domain of “fi- nance” the “financial institution” sense would be expected to be more likely than the “sloping land beside a body of river” sense. Unfortunately, it is not feasible to produce large manually sense- annotated corpora for every domain of interest. McCarthy et al. (2004) propose a method to pre- dict sense distributions from raw corpora and use this as a first sense heuristic for tagging text with the predominant sense. Rather than assigning pre- dominant sense in every case, our approach aims to use these sense distributions collected from do- main specific corpora as a knowledge source and combine this with information from the context. Our approach focuses on the strong influence of domain for WSD (Buitelaar et al., 2006) and the benefits of focusing on words salient to the do- main (Koeling et al., 2005). Words are assigned a ranking score based on its keyness (salience) in the given domain. We use these word scores as another knowledge source. Graph based methods have been shown to produce state-of-the-art performance for unsu- pervised word sense disambiguation (Agirre and Soroa, 2009; Sinha and Mihalcea, 2007). These approaches use well-known graph-based tech- niques to …

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