Term weighting is a crucial task in many Information Retrieval applications. Common approaches are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms. The Context Function is learned by examples, so that its implementation is mostly automatic. The algorithm was successfully tested on a data set of crossword clues, which represent a case of Single-Word Question Answering.

Ernandes, M., Angelini, G., Gori, M., Rigutini, L., Scarselli, F. (2006). Adaptive context-based term (re) weighting: an experiment on single-word question answering. In Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence (pp.819-820). Amsterdam : IOS PRESS.

Adaptive context-based term (re) weighting: an experiment on single-word question answering

GORI, MARCO;SCARSELLI, FRANCO
2006-01-01

Abstract

Term weighting is a crucial task in many Information Retrieval applications. Common approaches are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms. The Context Function is learned by examples, so that its implementation is mostly automatic. The algorithm was successfully tested on a data set of crossword clues, which represent a case of Single-Word Question Answering.
1586036424
978-1-58603-642-3
Ernandes, M., Angelini, G., Gori, M., Rigutini, L., Scarselli, F. (2006). Adaptive context-based term (re) weighting: an experiment on single-word question answering. In Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence (pp.819-820). Amsterdam : IOS PRESS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/3378
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