UNIBA: JIGSAW algorithm for word sense disambiguation

Authors:
P. Basile;M. de Gemmis;A. L. Gentile;P. Lops;G. Semeraro
Affiliations:
University of Bari, Bari, Italy;University of Bari, Bari, Italy;University of Bari, Bari, Italy;University of Bari, Bari, Italy;University of Bari, Bari, Italy
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

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Abstract

Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A breakthrough in this field would have a significant impact on many relevant web-based applications, such as information retrieval and information extraction. This paper describes JIGSAW, a knowledge-based WSD system that attemps to disambiguate all words in a text by exploiting WordNet senses. The main assumption is that a specific strategy for each Part-Of-Speech (POS) is better than a single strategy. We evaluated the accuracy of JIGSAW on SemEval-2007 task 1 competition. This task is an application-driven one, where the application is a fixed cross-lingual information retrieval system. Participants disambiguate text by assigning WordNet synsets, then the system has to do the expansion to other languages, index the expanded documents and run the retrieval for all the languages in batch. The retrieval results are taken as a measure for the effectiveness of the disambiguation.