Combining Sources of Evidence for Recognition of Relevant Passages in Texts

  • Alexander Gelbukh
  • NamO Kang
  • SangYong Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3563)


Automatically recognizing in large electronic texts short selfcontained passages relevant for a user query is necessary for fast and accurate information access to large text archives. Surprisingly, most search engines practically do not provide any help to the user in this tedious task, just presenting a list of whole documents supposedly containing the requested information. We show how different sources of evidence can be combined in order to assess the quality of different passages in a document and present the highest ranked ones to the user. Specifically, we take into account the relevance of a passage to the user query, structural integrity of the passage with respect to paragraphs and sections of the document, and topic integrity with respect to topic changes and topic threads in the text. Our experiments show that the results are promising.


Query Term User Query Question Answering Word Sense Disambiguation Document Retrieval 
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 2005

Authors and Affiliations

  • Alexander Gelbukh
    • 1
    • 2
  • NamO Kang
    • 1
  • SangYong Han
    • 1
  1. 1.Chung-Ang UniversityKorea
  2. 2.National Polytechnic InstituteMexico

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