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Efficient question classification and retrieval using category information and word embedding on cQA services

  • Kyoungman Bae
  • Youngjoong KoEmail author
Article
  • 12 Downloads

Abstract

Classifying the task of automatically assigning unlabeled questions into predefined categories (or topics) and effectively retrieving a similar question are crucial aspects of an effective cQA service. We first address the problems associated with estimating and utilizing the distribution of words in each category of word weights. We then apply an automatic expansion word generation technique that is based on our proposed weighting method and the pseudo relevance feedback to question classification. Secondly to address the lexical gap problem in question retrieval, the case frame of the sentence is first defined using the extracted components of a sentence, and a similarity measure based on the case frame and the word embedding is then derived to determine the similarities between two sentences. These similarities are then used to reorder the results of the first retrieval model. Consequently, the proposed methods significantly improve the performance of question classification and retrieval.

Keywords

Question classification Word weighting method Category information Pseudo-relevance feedback Question expansion 

Notes

Acknowledgments

This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2013-2-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Language Intelligence Research GroupElectronics and Telecommunications Research InstituteDaejeonRepublic of Korea
  2. 2.Department of Computer EngineeringDong-A University 840BusanRepublic of Korea

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