A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A stochastic finite-state word-segmentation algorithm for Chinese
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
The NYU system for MUC-6 or where's the syntax?
MUC6 '95 Proceedings of the 6th conference on Message understanding
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Named entity (NE) recognition is an important task for many natural language applications, such as Internet search engines, document indexing, information extraction and machine translation. Moreover, in oriental languages (such as Chinese, Japanese and Korean), NE recognition is even more important because it significantly affects the performance of word segmentation, the most fundamental task for understanding the texts in oriental languages. In this paper, a probabilistic verification model is designed for verifying the correctness of a named entity candidate. This model assesses the confidence level of a candidate not only according to the candidate's structure but also according to its context. In our design, the clues for confidence measurement are collected from both positive and negative examples in the training data in a statistical manner. Experimental results show that the proposed method significantly improves the F-measure of Chinese personal name recognition from 86.5% to 94.4%.