Using Semantic Constraints to Improve Question Answering
نویسندگان
چکیده
In this paper, we discuss our experience in using semantic constraints to improve the precision of a reformulation-based questionanswering system. First, we present a method for acquiring semanticbased 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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