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Statistical Substring Reduction in Linear Time

  • Xueqiang Lü
  • Le Zhang
  • Junfeng Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

We study the problem of efficiently removing equal frequency n-gram substrings from an n-gram set, formally called Statistical Substring Reduction (SSR). SSR is a useful operation in corpus based multi-word unit research and new word identification task of oriental language processing. We present a new SSR algorithm that has linear time (O(n)) complexity, and prove its equivalence with the traditional O(n 2) algorithm. In particular, using experimental results from several corpora with different sizes, we show that it is possible to achieve performance close to that theoretically predicated for this task. Even in a small corpus the new algorithm is several orders of magnitude faster than the O(n 2) one. These results show that our algorithm is reliable and efficient, and is therefore an appropriate choice for large scale corpus processing.

Keywords

Statistical Unit Statistical String Extraction Task Radix Sort Oriental Language 
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

  • Xueqiang Lü
    • 1
  • Le Zhang
    • 2
  • Junfeng Hu
    • 1
  1. 1.Institute of Computational LinguisticsPeking UniversityBeijing
  2. 2.Institute of Computer Software & TheoryNortheastern UniversityShenyang

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