Adaptive Context-based term (re)weightingAn experiment on Single-Word Question Answering

  • Authors:

  • Marco Ernandes;Giovanni Angelini;Marco Gori;Leonardo Rigutini;Franco Scarselli

  • Affiliations:

  • Dip. di Ingegneria dell'Informazione, Università di Siena, via Roma 56, 53100 -Siena --Italy, email: {ernandes, angelini,marco, rigutini, franco}@dii.unisi.it;Dip. di Ingegneria dell'Informazione, Università di Siena, via Roma 56, 53100 -Siena --Italy, email: {ernandes, angelini,marco, rigutini, franco}@dii.unisi.it;Dip. di Ingegneria dell'Informazione, Università di Siena, via Roma 56, 53100 -Siena --Italy, email: {ernandes, angelini,marco, rigutini, franco}@dii.unisi.it;Dip. di Ingegneria dell'Informazione, Università di Siena, via Roma 56, 53100 -Siena --Italy, email: {ernandes, angelini,marco, rigutini, franco}@dii.unisi.it;Dip. di Ingegneria dell'Informazione, Università di Siena, via Roma 56, 53100 -Siena --Italy, email: {ernandes, angelini,marco, rigutini, franco}@dii.unisi.it

  • Venue:
  • Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
  • Year:
  • 2006

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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.