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A Statistical Approach For Open Domain Question Answering

  • Abraham Ittycheriah
Part of the Text, Speech and Language Technology book series (TLTB, volume 32)

This chapter investigates a statistical approach to open domain question answering. Although the work presented in this chapter centers around maximum entropy models, the models required can be modelled using any machine learning approach. To perform question answering, as has been discussed in previous chapters, questions are first analyzed and a prediction is made as to what type of answer the user is expecting. Secondly, a fast search of the text database is performed and the top documents relevant to the query are retrieved. These documents have been annotated automatically using a named entity tagger. Finally, the answer tag prediction and the annotated documents are input to the answer selection stage. Results obtained from a trainable answer selection algorithm are reported.

Keywords

Maximum Entropy Parse Tree Question Answering Statistical Machine Translation Question Word 
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 2008

Authors and Affiliations

  • Abraham Ittycheriah
    • 1
  1. 1.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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