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    • Revista IJIMAI
    • 2013
    • vol. 2, nº 1, march 2013
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2013
    • vol. 2, nº 1, march 2013
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    A Case-based Reasoning Approach to Validate Grammatical Gender and Number Agreement in Spanish language

    Autor: 
    Bacca, Jorge
    ;
    Baldiris, Silvia
    ;
    Fabregat, Ramón
    ;
    Avila, Cecilia
    Fecha: 
    03/2013
    Palabra clave: 
    authoring tool; second language acquisition; indigenous people; case-based reasoning; local similarity functions; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/9662
    DOI: 
    http://dx.doi.org/10.9781/ijimai.2013.2110
    Dirección web: 
    https://www.ijimai.org/journal/node/423
    Open Access
    Resumen:
    Across Latin America 420 indigenous languages are spoken. Spanish is considered a second language in indigenous communities and is progressively introduced in education. However, most of the tools to support teaching processes of a second language have been developed for the most common languages such as English, French, German, Italian, etc. As a result, only a small amount of learning objects and authoring tools have been developed for indigenous people considering the specific needs of their population. This paper introduces Multilingual–Tiny as a web authoring tool to support the virtual experience of indigenous students and teachers when they are creating learning objects in indigenous languages or in Spanish language, in particular, when they have to deal with the grammatical structures of Spanish. Multilingual–Tiny has a module based on the Case-based Reasoning technique to provide recommendations in real time when teachers and students write texts in Spanish. An experiment was performed in order to compare some local similarity functions to retrieve cases from the case library taking into account the grammatical structures. As a result we found the similarity function with the best performance.
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