Learning question focus and semantically related features from Web search results for Chinese question classification

Shu Jung Lin, Wen Hsiang Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Recently, some machine learning techniques like support vector machines are employed for question classification. However, these techniques heavily depend on the availability of large amounts of training data, and may suffer many difficulties while facing various new questions from the real users on the Web. To mitigate the problem of lacking sufficient training data, in this paper, we present a simple learning method that explores Web search results to collect more training data automatically by a few seed terms (question answers). In addition, we propose a novel semantically related feature model (SRFM), which takes advantage of question focuses and their semantically related features learned from the larger number of collected training data to support the determination of question type. Our experimental results show that the proposed new learning method can obtain better classification performance than the bigram language modeling (LM) approach for the questions with untrained question focuses.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - Third Asia Information Retrieval Symposium, AIRS 2006, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)3540457801, 9783540457800
Publication statusPublished - 2006
Event3rd Asia Information Retrieval Symposium, AIRS 2006 - Singapore, Singapore
Duration: 2006 Oct 162006 Oct 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd Asia Information Retrieval Symposium, AIRS 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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