A Maximum Entropy Model Based Answer Extraction for Chinese Question Answering

  • Ang Sun
  • Minghu Jiang
  • Yanjun Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


We regard answer extraction of Question Answering (QA) system as a classification problem, classifying answer candidate sentences into positive or negative. To confirm the feasibility of this new approach, we first extract features concerning question sentences and answer words from question answer pairs (QA pair), then we conduct experiments based on these features, using Maximum Entropy Model (MEM) as a Machine Learning (ML) technique. The first experiment conducted on the class-TIME_YEAR achieves 81.24% in precision and 78.48% in recall. The second experiment expanded to two other classes-OBJ_SUBSTANCE and LOC_CONTINENT also shows good performance.


Query Word Combine Feature Question Answering Interrogative Word Maximum Entropy Model 
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

  • Ang Sun
    • 1
  • Minghu Jiang
    • 1
  • Yanjun Ma
    • 1
  1. 1.Computational Linguistics Lab, Dept. of Chinese LanguageTsinghua UniversityBeijingChina

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