Automatic assessment of students’ free-text answers underpinned by the combination of a BLEU-inspired algorithm and latent semantic analysis
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
American Association for Artificial IntelligenceFecha de edición
2005Cita
Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference. Ed. Ingrid Russell and Zdravko Markov. California: AAAI Press, 2005. 358-363ISBN
978-1-57735-234-1Financiado por
This work has been sponsored by the Spanish Ministry of Science and Technology, project number TIN2004-03140.Versión del editor
http://www.aaai.org/Library/FLAIRS/2005/flairs05-059.phpMaterias
InformáticaNota
This is an electronic version of the paper presented at the International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005Derechos
Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.Resumen
In previous work we have proved that the BLEU algorithm
(Papineni et al. 2001), originally devised for evaluating
Machine Translation systems, can be applied to
assessing short essays written by students. In this paper
we present a comparative evaluation between this
BLEU-inspired algorithm and a system based on Latent
Semantic Analysis. In addition we propose an effective
combination schema for them. Despite the simplicity of
these shallow NLP methods, they achieve state-of-theart
correlations to the teachers’ scores while keeping the
language-independence and without requiring any domain
specific knowledge.
Lista de ficheros
Google Scholar:Pérez Marín, Diana
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Gliozzo, Alfio
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Strapparava, Carlo
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Alfonseca Cubero, Enrique
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Rodríguez Marín, Pilar
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Magnini, Bernardo
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