The JIGSAW Algorithm for Word Sense Disambiguation and Semantic Indexing of Documents
Pierpaolo Basile, Marco de Gemmis, Anna Lisa Gentile, Pasquale Lops and Giovanni Semeraro
The 10th Congress of the Italian Association for Artificial Intelligence (AIIA 2007)
Roma, Italy, September 10-13, 2007
Abstract
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. In fact, a breakthrough in this field would have a significant impact on many relevant fields, such as information retrieval and information extraction. This paper describes JIGSAW, a knowledge-based WSD algorithm that attemps to disambiguate all words in a text by exploiting WordNetsenses. The main assumption is that a Part-Of-Speech (POS)-dependent strategy to WSD can turn out to be more effective than a unique strategy. Semantics provided by WSD gives an added value to applications centred on humans as users. Two empirical evaluations are described in the paper. First, we evaluated the accuracy of JIGSAW on Task 1 of SEMEVAL-1 competition. This task measures the effectiveness of a WSD algorithm in an Information Retrieval System. For the second evaluation, we used semantically indexed documents obtained through a WSD process in order to train a na\"ive Bayes learner that infers “semantic” sense-based user profiles as binary text classifiers. The goal of the second empirical evaluation has been to measure the accuracy of the user profiles in selecting relevant documents to be recommended within a document collection.