UoY: Graphs of Unambiguous Vertices for Word Sense Induction and Disambiguation

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 355–358, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics UoY: Graphs of Unambiguous Vertices for Word Sense Induction and Disambiguation Ioannis Korkontzelos, Suresh Manandhar Department of Computer Science The University of York Heslington, York, YO10 5NG, UK {johnkork, suresh}@cs.york.ac.uk Abstract This paper presents an unsupervised graph-based method for automatic word sense induction and disambiguation. The innovative part of our method is the as- signment of either a word or a word pair to each vertex of the constructed graph. Word senses are induced by clustering the constructed graph. In the disambiguation stage, each induced cluster is scored ac- cording to the number of its vertices found in the context of the target word. Our sys- tem participated in SemEval-2010 word sense induction and disambiguation task. 1 Introduction There exists significant evidence that word sense disambiguation is important for a variety of nat- ural language processing tasks: machine transla- tion, information retrieval, grammatical analysis, speech and text processing (Veronis, 2004). How- ever, the “fixed-list” of senses paradigm, where the senses of a target word is a closed list of defini- tions coming from a standard dictionary (Agirre et al., 2006), was long ago abandoned. The rea- son is that sense lists, such as WordNet (Miller, 1995), miss many senses, especially domain- specific ones (Pantel and Lin, 2002). The miss- ing concepts are not recognised. Moreover, senses cannot be easily related to their use in context. Word sense induction methods can be divided into vector-space models and graph based ones. In a vector-space model, each context of a target word is represented as a feature vector, e.g. fre- quency of cooccurring words (Katz and Gies- brecht, 2006). Context vectors are clustered and the resulting clusters represent the induced senses. Recently, graph-based methods have been em- ployed for word sense induction (Agirre and Soroa, 2007). Typically, graph-based methods represent each context word of the target word as a vertex. Two vertices are connected via an edge if they cooccur in one or more instances. Once the cooccurrence graph has been constructed, dif- ferent graph clustering algorithms are applied to partition the graph. Each cluster (partition) con- sists of a set of words that are semantically related to the particular sense (Veronis, 2004). The poten- tial advantage of graph-based methods is that they can combine both local and global cooccurrence information (Agirre et al., 2006). Klapaftis and Manandhar (2008) presented a graph-based approach that represents pairs of words as vertices instead of single words. They claimed that single words might appear with more than one senses of the target word, while they hy- pothesize that a pair of words is unambiguous. Hard-clustering the graph will potentially identify less conflating senses of the target word. In this paper, we relax the above hypothesis be- cause in some cases a single word is unambiguous. We present a method that generates two-word ver- tices only when a single word vertex is unambigu- ous. …

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