Abstract
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).
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Supported by the National Natural Science Foundation of China (Grant Nos. 60473138 and 60675035) and the Beijing Natural Science Foundation (Grant No. 4072012).
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Sun, X., Wang, HF. & Wang, B. Predicting Chinese Abbreviations from Definitions: An Empirical Learning Approach Using Support Vector Regression. J. Comput. Sci. Technol. 23, 602–611 (2008). https://doi.org/10.1007/s11390-008-9156-5
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DOI: https://doi.org/10.1007/s11390-008-9156-5