info:eu-repo/semantics/publishedVersion
A Supervised Term-Weighting Method and its Application to Variable Extraction from Digital Media
Fecha
2018Registro en:
A Supervised Term-Weighting Method and its Application to Variable Extraction from Digital Media; XIX Simposio Argentino de Inteligencia Artificial; Buenos Aires; Argentina; 2018; 40-53
2451-7585
CONICET Digital
CONICET
Autor
Maisonnave, Mariano
Delbianco, Fernando Andrés
Tohmé, Fernando Abel
Maguitman, Ana Gabriela
Resumen
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 potential of the proposal as a first step for identifying different types of associations between words.