Improving Full Text Search with Text Mining Tools

  • Scott Piao
  • Brian Rea
  • John McNaught
  • Sophia Ananiadou
Conference paper

DOI: 10.1007/978-3-642-12550-8_29

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5723)
Cite this paper as:
Piao S., Rea B., McNaught J., Ananiadou S. (2010) Improving Full Text Search with Text Mining Tools. In: Horacek H., Métais E., Muñoz R., Wolska M. (eds) Natural Language Processing and Information Systems. NLDB 2009. Lecture Notes in Computer Science, vol 5723. Springer, Berlin, Heidelberg

Abstract

Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.

Keywords

Information Retrieval Full Text Search Term extraction Termine Document clustering Natural Language processing 

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Scott Piao
    • 1
  • Brian Rea
    • 1
  • John McNaught
    • 1
  • Sophia Ananiadou
    • 1
  1. 1.National Centre for Text Mining, School of Computer ScienceThe University of ManchesterManchesterUK

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