Evaluation of LSA performance in Spanish using multiple corpus of text

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 an...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Carrillo, Facundo, Cecchi, Guillermo, Sigman, Mariano, Fernández Slezak, Diego
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2013
Materias:	Ciencias Informáticas Latent Semantic Analysis regional Spanish corpus Natural Language Processing
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/76358 http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/ASAI/18.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-76358
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Latent Semantic Analysis regional Spanish corpus Natural Language Processing
spellingShingle	Ciencias Informáticas Latent Semantic Analysis regional Spanish corpus Natural Language Processing Carrillo, Facundo Cecchi, Guillermo Sigman, Mariano Fernández Slezak, Diego Evaluation of LSA performance in Spanish using multiple corpus of text
topic_facet	Ciencias Informáticas Latent Semantic Analysis regional Spanish corpus Natural Language Processing
description	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.
format	Objeto de conferencia Objeto de conferencia
author	Carrillo, Facundo Cecchi, Guillermo Sigman, Mariano Fernández Slezak, Diego
author_facet	Carrillo, Facundo Cecchi, Guillermo Sigman, Mariano Fernández Slezak, Diego
author_sort	Carrillo, Facundo
title	Evaluation of LSA performance in Spanish using multiple corpus of text
title_short	Evaluation of LSA performance in Spanish using multiple corpus of text
title_full	Evaluation of LSA performance in Spanish using multiple corpus of text
title_fullStr	Evaluation of LSA performance in Spanish using multiple corpus of text
title_full_unstemmed	Evaluation of LSA performance in Spanish using multiple corpus of text
title_sort	evaluation of lsa performance in spanish using multiple corpus of text
publishDate	2013
url	http://sedici.unlp.edu.ar/handle/10915/76358 http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/ASAI/18.pdf
work_keys_str_mv	AT carrillofacundo evaluationoflsaperformanceinspanishusingmultiplecorpusoftext AT cecchiguillermo evaluationoflsaperformanceinspanishusingmultiplecorpusoftext AT sigmanmariano evaluationoflsaperformanceinspanishusingmultiplecorpusoftext AT fernandezslezakdiego evaluationoflsaperformanceinspanishusingmultiplecorpusoftext
bdutipo_str	Repositorios
_version_	1764820488117288961

Evaluation of LSA performance in Spanish using multiple corpus of text

Ejemplares similares