Automatic assessment of students’ free-text answers underpinned by the combination of a BLEU-inspired algorithm and latent semantic analysis
EntityUAM. Departamento de Ingeniería Informática
PublisherAmerican Association for Artificial Intelligence
CitationProceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference. Ed. Ingrid Russell and Zdravko Markov. California: AAAI Press, 2005. 358-363
Funded byThis work has been sponsored by the Spanish Ministry of Science and Technology, project number TIN2004-03140.
NoteThis is an electronic version of the paper presented at the International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
RightsCopyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
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.
Google Scholar:Pérez Marín, Diana - Gliozzo, Alfio - Strapparava, Carlo - Alfonseca Cubero, Enrique - Rodríguez Marín, Pilar - Magnini, Bernardo
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