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Enhancing QA Systems with Complex Temporal Question Processing Capabilities

Authors :
Saquete, Estela
Vicedo, Jose Luis
Martínez-Barco, Patricio
Muñoz, Rafael
Llorens, Hector
Source :
Journal Of Artificial Intelligence Research, Volume 35, pages 775-811, 2009
Publication Year :
2014

Abstract

This paper presents a multilayered architecture that enhances the capabilities of current QA systems and allows different types of complex questions or queries to be processed. The answers to these questions need to be gathered from factual information scattered throughout different documents. Specifically, we designed a specialized layer to process the different types of temporal questions. Complex temporal questions are first decomposed into simple questions, according to the temporal relations expressed in the original question. In the same way, the answers to the resulting simple questions are recomposed, fulfilling the temporal restrictions of the original complex question. A novel aspect of this approach resides in the decomposition which uses a minimal quantity of resources, with the final aim of obtaining a portable platform that is easily extensible to other languages. In this paper we also present a methodology for evaluation of the decomposition of the questions as well as the ability of the implemented temporal layer to perform at a multilingual level. The temporal layer was first performed for English, then evaluated and compared with: a) a general purpose QA system (F-measure 65.47% for QA plus English temporal layer vs. 38.01% for the general QA system), and b) a well-known QA system. Much better results were obtained for temporal questions with the multilayered system. This system was therefore extended to Spanish and very good results were again obtained in the evaluation (F-measure 40.36% for QA plus Spanish temporal layer vs. 22.94% for the general QA system).

Details

Database :
arXiv
Journal :
Journal Of Artificial Intelligence Research, Volume 35, pages 775-811, 2009
Publication Type :
Report
Accession number :
edsarx.1401.3482
Document Type :
Working Paper
Full Text :
https://doi.org/10.1613/jair.2805