UMND1: Unsupervised Word Sense Disambiguation Using Contextual Semantic Relatedness

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 390–393, Prague, June 2007. c⃝2007 Association for Computational Linguistics UMND1: Unsupervised Word Sense Disambiguation Using Contextual Semantic Relatedness Siddharth Patwardhan School of Computing University of Utah Salt Lake City, UT 84112. sidd@cs.utah.edu Satanjeev Banerjee Language Technologies Inst. Carnegie Mellon University Pittsburgh, PA 15217. banerjee@cs.cmu.edu Ted Pedersen Dept. of Computer Science University of Minnesota Duluth, MN 55812. tpederse@d.umn.edu Abstract In this paper we describe an unsuper- vised WordNet-based Word Sense Disam- biguation system, which participated (as UMND1) in the SemEval-2007 Coarse- grained English Lexical Sample task. The system disambiguates a target word by using WordNet-based measures of semantic relat- edness to find the sense of the word that is semantically most strongly related to the senses of the words in the context of the tar- get word. We briefly describe this system, the configuration options used for the task, and present some analysis of the results. 1 Introduction WordNet::SenseRelate::TargetWord1 (Patwardhan et al., 2005; Patwardhan et al., 2003) is an unsuper- vised Word Sense Disambiguation (WSD) system, which is based on the hypothesis that the intended sense of an ambiguous word is related to the words in its context. For example, if the “financial institution” sense of bank is intended in a context, then it is highly likely the context would contain related words such as money, transaction, interest rate, etc. The algorithm, therefore, determines the intended sense of a word (target word) in a given context by measuring the relatedness of each sense of that word with the words in its context. The sense of the target word that is most related to its context is selected as the intended sense of the target word. The system uses WordNet-based 1http://senserelate.sourceforge.net measures of semantic relatedness2 (Pedersen et al., 2004) to measure the relatedness between the different senses of the target word and the words in its context. This system is completely unsupervised and re- quires no annotated data for training. The lexical database WordNet (Fellbaum, 1998) is the only re- source that the system uses to measure the related- ness between words and concepts. Thus, our system is classified under the closed track of the task. 2 System Description Our WSD system consists of a modular framework, which allows different algorithms for the different subtasks to be plugged into the system. We divide the disambiguation task into two primary subtasks: context selection and sense selection. The context selection module tries to select words from the con- text that are most likely to be indicative of the sense of the target word. The sense selection module then uses the set of selected context words to choose one of the senses of the target word as the answer. Figure 1 shows a block schematic of the system, which takes SemEval-2007 English Lexical Sample instances as input. Each instance is a made up of a few English sentences, and one word from these sentences is marked as the target word to be dis- ambiguated. …

Content truncated to 500 words