Automatic Term Extraction Using Log-Likelihood Based Comparison with General Reference Corpus

  • Alexander Gelbukh
  • Grigori Sidorov
  • Eduardo Lavin-Villa
  • Liliana Chanona-Hernandez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6177)

Abstract

In the paper we present a method that allows an extraction of single-word terms for a specific domain. At the next stage these terms can be used as candidates for multi-word term extraction. The proposed method is based on comparison with general reference corpus using log-likelihood similarity. We also perform clustering of the extracted terms using k-means algorithm and cosine similarity measure. We made experiments using texts of the domain of computer science. The obtained term list is analyzed in detail.

Keywords

Single-word term extraction log-likelihood reference corpus term clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Gelbukh
    • 1
  • Grigori Sidorov
    • 1
  • Eduardo Lavin-Villa
    • 1
  • Liliana Chanona-Hernandez
    • 2
  1. 1.Center for Computing Research (CIC)National Polytechnic Institute (IPN)MexicoMexico
  2. 2.Engineering faculty (ESIME)National Polytechnic Institute (IPN)MexicoMexico

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