Abstract
This paper describes a QA system centered in a full data-driven architecture. It applies machine learning and text mining techniques to identify the most probable answers to factoid and definition questions respectively. Its major quality is that it mainly relies on the use of lexical information and avoids applying any complex language processing resources such as named entity classifiers, parsers and ontologies. Experimental results on the Spanish Question Answering task at CLEF 2006 show that the proposed architecture can be a practical solution for monolingual question answering by reaching a precision as high as 51%.
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Juárez-González, A., Téllez-Valero, A., Denicia-Carral, C., Montes-y-Gómez, M., Villaseñor-Pineda, L. (2007). Using Machine Learning and Text Mining in Question Answering. In: Peters, C., et al. Evaluation of Multilingual and Multi-modal Information Retrieval. CLEF 2006. Lecture Notes in Computer Science, vol 4730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74999-8_49
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DOI: https://doi.org/10.1007/978-3-540-74999-8_49
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