Answer Extraction as Sequence Tagging with Tree Edit Distance

  • Yao X
  • Van Durme B
  • Callison-Burch C
 et al. 
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Our goal is to extract answers from pre- retrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types. This casts answer extraction as an an- swer sequence tagging problem for the first time, where knowledge of shared structure be- tween question and source sentence is incor- porated through features based on Tree Edit Distance (TED). Our model is free of man- ually created question and answer templates, fast to run (processing 200 QA pairs per sec- ond excluding parsing time), and yields an F1 of 63.3% on a new public dataset based on prior TREC QA evaluations. The developed system is open-source, and includes an imple- mentation of the TED model that is state of the art in the task of ranking QA pairs.

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  • ISBN: 9781937284473
  • SCOPUS: 2-s2.0-84916213998
  • PUI: 603547709
  • PMID: 895224
  • SGR: 84916213998


  • Xuchen Yao

  • Benjamin Van Durme

  • Chris Callison-Burch

  • Peter Clark

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