Abstract
In this paper, we discuss our experience in using semantic constraints to improve the precision of a reformulation-based question-answering system. First, we present a method for acquiring semantic-based reformulations automatically. The goal is to generate patterns from sentences retrieved from the Web based on syntactic and semantic constraints. Once these constraints have been defined, we present a method to evaluate and re-rank candidate answers that satisfy these constraints using redundancy. The two approaches have been evaluated independently and in combination. The evaluation on about 500 questions from TREC-11 shows that the acquired semantic patterns increase the precision by 16% and the MRR by 26%, the re-ranking using semantic redundancy as well as the combined approach increase the precision by about 30% and the MRR by 67%. This shows that no manual work is now necessary to build question reformulations; while still increasing performance
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Yousefi, J., Kosseim, L. (2006). Using Semantic Constraints to Improve Question Answering. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2006. Lecture Notes in Computer Science, vol 3999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11765448_11
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DOI: https://doi.org/10.1007/11765448_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34616-6
Online ISBN: 978-3-540-34617-3
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