A stochastic finite-state word-segmentation algorithm for Chinese
Computational Linguistics
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
A trainable rule-based algorithm for word segmentation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Japanese named entity recognition based on a simple rule generator and decision tree learning
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Mining events and new name translations from online daily news
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Chinese lexical analysis using hierarchical hidden Markov model
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Named entity translation matching and learning: With application for mining unseen translations
ACM Transactions on Information Systems (TOIS)
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Chinese NE (Named Entity) recognition is a difficult problem because of the uncertainty in word segmentation and flexibility in language structure. This paper proposes the use of a rationality model in a multi-agent framework to tackle this problem. We employ a greedy strategy and use the NE rationality model to evaluate and detect all possible NEs in the text. We then treat the process of selecting the best possible NEs as a multi-agent negotiation problem. The resulting system is robust and is able to handle different types of NE effectively. Our test on the MET-2 test corpus indicates that our system is able to achieve high F1 values of above 92% on all NE types.