Using machine learning and text mining in question answering

Authors:
Antonio Juárez-González;Alberto Téllez-Valero;Claudia Denicia-Carral;Manuel Montes-y-Gómez;Luis Villaseñor-Pineda
Affiliations:
Language Technologies Group, Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Mexico;Language Technologies Group, Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Mexico;Language Technologies Group, Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Mexico;Language Technologies Group, Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Mexico;Language Technologies Group, Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Mexico
Venue:
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Year:
2006

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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%.