A maximum entropy approach to natural language processing
Computational Linguistics
Chinese named entity identification using class-based language model
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improved source-channel models for Chinese word segmentation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
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Single character named entity (SCNE) is a name entity (NE) composed of one Chinese character, such as "[Abstract contained text which could not be captured.]" (zhong1, China) and "[Abstract contained text which could not be captured.]" (e2, Russia). SCNE is very common in written Chinese text. However, due to the lack of in-depth research, SCNE is a major source of errors in named entity recognition (NER). This paper formulates the SCNE recognition within the source-channel model framework. Our experiments show very encouraging results: an F-score of 81.01% for single character location name recognition, and an F-score of 68.02% for single character person name recognition. An alternative view of the SCNE recognition problem is to formulate it as a classification task. We construct two classifiers based on maximum entropy model (ME) and vector space model (VSM), respectively. We compare all proposed approaches, showing that the source-channel model performs the best in most cases.