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Using Clustering Approaches to Open-Domain Question Answering

  • Youzheng Wu
  • Hideki Kashioka
  • Jun Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)

Abstract

This paper presents two novel clustering approaches and their application to open-domain question answering. The One-Sentence-Multi-Topic clustering approach is first presented, which clusters sentences to improve the language model for retrieving sentences. Second, regarding each cluster in the results for One-Sentence-Multi-Topic clustering as aligned sentences, we present a pattern-similarity-based clustering approach that automatically learns syntactic answer patterns to answer selection through vertical and horizontal clustering. Our experiments on Chinese question answering demonstrates that One-Sentence-Multi-Topic clustering is much better than K-Means and is comparable to PLSI when used in sentence clustering of question answering. Similarly, the pattern-similarity-based clustering also proved to be efficient in learning syntactic answer patterns, the absolute improvement in syntactic pattern-based answer extraction over retrieval-based answer extraction is about 9%.

Keywords

Language Model Cluster Approach Pattern Cluster Question Type Question Answering 
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 2007

Authors and Affiliations

  • Youzheng Wu
    • 1
    • 2
  • Hideki Kashioka
    • 1
  • Jun Zhao
    • 2
  1. 1.NiCT-ATR 2-2-2 Hikaridai “Keihanna Science City” Kyoto 619-0288Japan
  2. 2.NLPR CASIA No.95 Zhongguancun East Road Beijing 100080China

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