An Efficient Minimum Vocabulary Construction Algorithm for Language Modeling

  • Sina Lin
  • Zengchang Qin
  • Zehua Huang
  • Tao Wan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)

Abstract

In learning a new word by a dictionary, we first need to know a set of “basic words” which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification.

Keywords

Language Modeling Word Frequency Basic Word Word Level Complex Word 
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 2012

Authors and Affiliations

  • Sina Lin
    • 1
  • Zengchang Qin
    • 1
    • 3
  • Zehua Huang
    • 1
    • 2
  • Tao Wan
    • 4
  1. 1.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina
  2. 2.School of Advanced EngineeringBeihang UniversityChina
  3. 3.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  4. 4.School of MedicineBoston UniversityBostonUSA

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