A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval

Authors

  • Mariano Maisonnave Universidad Nacional del Sur
  • Fernando Delbianco
  • Fernando Abel Tohmé
  • Ana Gabriela Maguitman

DOI:

https://doi.org/10.4114/intartif.vol22iss63pp61-80

Keywords:

Term Weighting, Variable Extraction, Information Retrieval, Query-Term Selection

Abstract

Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables.  This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of  retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the applicability of the proposed technique to address diverse problems such as building prediction models, supporting knowledge modeling, and achieving total recall.

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Published

2019-02-27

How to Cite

Maisonnave, M., Delbianco, F., Tohmé, F. A., & Maguitman, A. G. (2019). A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval. Inteligencia Artificial, 22(63), 61-80. https://doi.org/10.4114/intartif.vol22iss63pp61-80