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A Pattern Learning Approach to Question Answering Within the Ephyra Framework

  • Nico Schlaefer
  • Petra Gieselmann
  • Thomas Schaaf
  • Alex Waibel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)

Abstract

This paper describes the Ephyra question answering engine, a modular and extensible framework that allows to integrate multiple approaches to question answering in one system. Our framework can be adapted to languages other than English by replacing language-specific components. It supports the two major approaches to question answering, knowledge annotation and knowledge mining. Ephyra uses the web as a data resource, but could also work with smaller corpora. In addition, we propose a novel approach to question interpretation which abstracts from the original formulation of the question. Text patterns are used to interpret a question and to extract answers from text snippets. Our system automatically learns the patterns for answer extraction, using question-answer pairs as training data. Experimental results revealed the potential of this approach.

Keywords

Question Answering Query Formation Answer Pattern Context Object Query String 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nico Schlaefer
    • 1
  • Petra Gieselmann
    • 1
  • Thomas Schaaf
    • 2
  • Alex Waibel
    • 1
    • 2
  1. 1.Interactive Systems LabsITI, Universität KarlsruheKarlsruheGermany
  2. 2.Interactive Systems LabsCarnegie Mellon UniversityPittsburghUSA

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