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dc.date.accessioned 2019-06-12T17:49:28Z
dc.date.available 2019-06-12T17:49:28Z
dc.date.issued 2013
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/76358
dc.description.abstract Latent Semantic Analysis is a natural language processing tools that allows estimating semantic distance between terms. The success of LSA is mainly based on the training corpus choice, which have been studied principally in English. This study focuses on studying LSA with regional Spanish corpus and evaluate the performance by identifying synonyms. We found that performance was slightly better than chance, concordantly with previous results. Standard LSA method cannot dynamically increase the training corpus. By using classifiers we combined multiple LSA models and showed that the use of automatic classifiers increase the performance. en
dc.format.extent 198-201 es
dc.language en es
dc.subject Latent Semantic Analysis en
dc.subject Natural Language Processing es
dc.subject regional Spanish corpus en
dc.title Evaluation of LSA performance in Spanish using multiple corpus of text en
dc.type Objeto de conferencia es
sedici.identifier.uri http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/ASAI/18.pdf es
sedici.identifier.issn 1850-2784 es
sedici.creator.person Carrillo, Facundo es
sedici.creator.person Cecchi, Guillermo es
sedici.creator.person Sigman, Mariano es
sedici.creator.person Fernández Slezak, Diego es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-sa/4.0/
sedici.date.exposure 2013-09
sedici.relation.event XIV Argentine Symposium on Artificial Intelligence (ASAI) - JAIIO 42 (2013). es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)