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Open-Domain Non-factoid Question Answering

  • Maria KhvalchikEmail author
  • Anagha Kulkarni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

We present an end-to-end system for open-domain non-factoid question answering. We leverage the information on the ever-growing World Wide Web, and the capabilities of modern search engines to find the relevant information. Our QA system is composed of three components: (i) query formulation module (QFM) (ii) candidate answer generation module (CAGM) and (iii) answer selection module (ASM). A thorough empirical evaluation using two datasets demonstrates that the proposed approach is highly competitive.

Keywords

Question answering Learning to rank Neural network BLSTM 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentSan Francisco State UniversitySan FranciscoUSA

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