Algorithms for bigram and trigram word clustering
Speech Communication
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An efficient method for determining bilingual word classes
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
A tutorial on support vector regression
Statistics and Computing
SaRAD: a Simple and Robust Abbreviation Dictionary
Bioinformatics
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ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Chinese abbreviation-definition identification: a SVM approach using context information
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Chinese abbreviation identification using abbreviation-template features and context information
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
A hybrid approach to chinese abbreviation expansion
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Chinese new word identification: a latent discriminative model with global features
Journal of Computer Science and Technology - Special issue on natural language processing
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
ACM Transactions on Asian Language Information Processing (TALIP)
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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).