Special Issue: Artificial Intelligence and Pattern Recognition

• Machine Learning and Data Mining • Previous Articles     Next Articles

Predicting Chinese Abbreviations from Definitions: An Empirical Learning Approach Using Support Vector Regression

Xu Sun1, 2, Hou-Feng Wang1, and Bo Wang1   

  1. 1Institute of Computational Linguistics, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China 2Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-0033, Japan
  • Received:2007-05-08 Revised:2008-04-02 Online:2008-07-10 Published:2008-07-10

In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however, make keyword-based approaches less effective. This paper presents an empirical learning approach to Chinese abbreviation prediction. In this study, each abbreviation is taken as a reduced form of the corresponding definition (expanded form), and the abbreviation prediction is formalized as a scoring and ranking problem among abbreviation candidates, which are automatically generated from the corresponding definition. By employing Support Vector Regression (SVR) for scoring, we can obtain multiple abbreviation candidates together with their SVR values, which are used for candidate ranking. Experimental results show that the SVR method performs better than the popular heuristic rule of abbreviation prediction. In addition, in abbreviation prediction, the SVR method outperforms the hidden Markov model (HMM).

Key words: WFMS; Task Agent; TaskActivator; multi-TaskDomain architecture;


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