UCF-WS: Domain Word Sense Disambiguation Using Web Selectors

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 392–395, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics UCF-WS: Domain Word Sense Disambiguation using Web Selectors Hansen A. Schwartz and Fernando Gomez School of Electrical Engineering and Computer Science University of Central Florida Orlando, FL 32816 {hschwartz,gomez}@cs.ucf.edu Abstract This paper studies the application of the Web Selectors word sense disambiguation system on a specific domain. The system was primarily applied without any domain tuning, but the incorporation of domain predominant sense information was ex- plored. Results indicated that the system performs relatively the same with domain predominant sense information as without, scoring well above a random baseline, but still 5 percentage points below results of using the first sense. 1 Introduction We explore the use of the Web Selectors word sense disambiguation system for disambiguating nouns and verbs of a domain text. Our method to acquire selectors from the Web for WSD was first described in (Schwartz and Gomez, 2008). The system is extended for the all-words domain task by including part of speech tags from the Stanford Parser (Klein and Manning, 2003). Additionally, a domain adaptation technique of using domain pre- dominant senses (Koeling et al., 2005) is explored, but our primary goal is concerned with evaluating the performance of the existing Web Selectors sys- tem on domain text. In previous studies, the Web Selectors system was applied to text of a general domain. However, the system was not directly tuned for the general domain. The system may perform just as strong for domain WSD since the selectors, which are the core of disambiguation, can come from any do- main present on the Web. In this paper, we study the application of the Web Selectors WSD algo- rithm to an all-words task on a specific domain, the SemEval 2010: Task 17 (Agirre et al., 2010). 2 Web Selectors Selectors are words which take the place of a given target word within its local context (Lin, 1997). In the case of acquiring selectors from the Web, we search with the text of local context (Schwartz and Gomez, 2008). For example, if one was search- ing for selectors of ‘channel’ in the sentence, “The navigation channel undergoes major shifts from north to south banks”, then a search query would be: The navigation * undergoes major shifts from north to south banks . where * represents a wildcard to match every se- lector. The query is shortened to produce more results until at least 300 selectors are acquired or the query is less than 6 words. The process of acquiring selectors repeats for every content word of the sentence. Example selectors that might be returned for ‘channel’ include ‘route’, ‘pathway’, and ‘passage’. Selectors serve for the system to essentially learn the areas or concepts of WordNet that the sense of a word should be similar or related. The target noun or verb is disambiguated by comparing its senses with all selectors for itself (target selec- tors), as …

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