dc.creator | Maisonnave, Mariano | |
dc.creator | Delbianco, Fernando Andrés | |
dc.creator | Tohmé, Fernando Abel | |
dc.creator | Maguitman, Ana Gabriela | |
dc.date.accessioned | 2019-12-23T17:50:53Z | |
dc.date.accessioned | 2022-10-15T07:08:33Z | |
dc.date.available | 2019-12-23T17:50:53Z | |
dc.date.available | 2022-10-15T07:08:33Z | |
dc.date.created | 2019-12-23T17:50:53Z | |
dc.date.issued | 2019-02 | |
dc.identifier | Maisonnave, Mariano; Delbianco, Fernando Andrés; Tohmé, Fernando Abel; Maguitman, Ana Gabriela; A flexible supervised term-weighting technique and its application to variable extraction and information retrieval; Iberamia; Inteligencia Artificial; 22; 63; 2-2019; 61-80 | |
dc.identifier | 1137-3601 | |
dc.identifier | http://hdl.handle.net/11336/92800 | |
dc.identifier | 1988-3064 | |
dc.identifier | CONICET Digital | |
dc.identifier | CONICET | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4358768 | |
dc.description.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. | |
dc.language | eng | |
dc.publisher | Iberamia | |
dc.relation | info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/index.php/intartif/article/view/255 | |
dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.4114/intartif.vol22iss63pp61-80 | |
dc.rights | https://creativecommons.org/licenses/by-nc/2.5/ar/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | INFORMATION RETRIEVAL | |
dc.subject | QUERY-TERM SELECTION | |
dc.subject | TERM WEIGHTING | |
dc.subject | VARIABLE EXTRACTION | |
dc.title | A flexible supervised term-weighting technique and its application to variable extraction and information retrieval | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:ar-repo/semantics/artículo | |
dc.type | info:eu-repo/semantics/publishedVersion | |