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Abstract

This paper presents an approach to build a novel two-level collocation net, which enables calculation of the collocation relationship between any two words, from a large raw corpus. The first level consists of atomic classes (each atomic class consists of one word and feature bigram), which are clustered into the second level class set. Each class in both levels is represented by its collocation candidate distribution, extracted from the linguistic analysis of the raw training corpus, over possible collocation relation types. In this way, all the information extracted from the linguistic analysis is kept in the collocation net. Our approach applies to both frequently and less-frequently occurring words by providing a clustering mechanism resolve the data sparseness problem through the collocation net. Experimentation shows that the collocation net is efficient and effective in solving the data sparseness problem and determining the collocation relationship between any two words.

Keywords

Parse Tree Linguistic Analysis Atomic Class Data Sparseness Problem Statistical Natural Language Processing 
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 2006

Authors and Affiliations

  • GuoDong Zhou
    • 1
    • 2
  • Min Zhang
    • 2
  • GuoHong Fu
    • 3
  1. 1.School of Computer Science and TechnologySuzhou UniversityChina
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.Department of LinguisticsThe University of Hong KongHong Kong

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