Learning pattern rules for Chinese named entity extraction
Eighteenth national conference on Artificial intelligence
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
GE: description of the NLTooLSET system as used for MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Chinese named entity identification using class-based language model
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Design of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
MITRE: description of the Alembic system used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A unified statistical model for the identification of English baseNP
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Chinese Named Entity Recognition combining a statistical model with human knowledge
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
Mining redundancy in candidate-bearing snippets to improve web question answering
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Applying Machine Learning to Chinese Entity Detection and Tracking
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Using Clustering Approaches to Open-Domain Question Answering
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Empirical study on the performance stability of named entity recognition model across domains
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Fusion of multiple features for chinese named entity recognition based on CRF model
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of each other. Secondly, in order to reduce the searching space and improve the efficiency, we introduce heuristic human knowledge into statistical model, which could increase the performance of NER significantly. Thirdly, we use three sub-models to respectively describe three kinds of transliterated person name, that is, Japanese, Russian and Euramerican person name, which can improve the performance of PN recognition. From the experimental results on People's Daily testing data, we can conclude that our Hybrid Model is better than the models which only use one kind of features. And the experiments on MET-2 testing data also confirm the above conclusion, which show that our algorithm has consistence on different testing data.