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Answer Type Identification for Question Answering

Supervised Learning of Dependency Graph Patterns from Natural Language Questions
  • Andrew D. Walker
  • Panos Alexopoulos
  • Andrew Starkey
  • Jeff Z. Pan
  • José Manuel Gómez-Pérez
  • Advaith Siddharthan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)

Abstract

Question Answering research has long recognised that the identification of the type of answer being requested is a fundamental step in the interpretation of a question as a whole. Previous strategies have ranged from trivial keyword matches, to statistical analyses, to well-defined algorithms based on shallow syntactic parses with user-interaction for ambiguity resolution. A novel strategy combining deep NLP on both syntactic and dependency parses with supervised learning is introduced and results that improve on extant alternatives reported. The impact of the strategy on QALD is also evaluated with a proprietary Question Answering system and its positive results analysed.

Keywords

Dependency Graph Question Answering Semantic Class Grammatical Structure Question Focus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research has been partly funded by the European Commission within the 7th Framework Programme/Marie Curie Industry-Academia Partnerships and Pathways schema/PEOPLE Work Programme 2011 project K-Drive number 286348 (cf. http://www.kdrive-project.eu).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrew D. Walker
    • 1
  • Panos Alexopoulos
    • 2
  • Andrew Starkey
    • 1
  • Jeff Z. Pan
    • 1
  • José Manuel Gómez-Pérez
    • 2
  • Advaith Siddharthan
    • 1
  1. 1.University of AberdeenAberdeenUK
  2. 2.Expert SystemAmsterdamNetherlands

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