Abbreviation recognition with maxent model

Authors:
Chunyu Kit;Xiaoyue Liu;Jonathan J. Webster
Affiliations:
Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong;Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong;Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong
Venue:
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Year:
2006

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Abstract

Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-initial words and candidate word length for performance enhancement. Experimental results show that this approach achieves impressive performance on the WSJ corpus.