An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
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
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A simple named entity extractor using AdaBoost
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Memory-based named entity recognition using unannotated data
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Chinese named entity recognition using lexicalized HMMs
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Detecting, categorizing and clustering entity mentions in Chinese text
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
Applying Machine Learning to Chinese Entity Detection and Tracking
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
Domain adaptation with latent semantic association for named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Semi-joint labeling for chinese named entity recognition
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Using deep belief nets for Chinese named entity categorization
NEWS '10 Proceedings of the 2010 Named Entities Workshop
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This paper presents a Chinese named entity recognition system that employs the Robust Risk Minimization (RRM) classification method and incorporates the advantages of character-based and word-based models. From experiments on a large-scale corpus, we show that significant performance enhancements can be obtained by integrating various linguistic information (such as Chinese word segmentation, semantic types, part of speech, and named entity triggers) into a basic Chinese character based model. A novel feature weighting mechanism is also employed to obtain more useful cues from most important linguistic features. Moreover, to overcome the limitation of computational resources in building a high-quality named entity recognition system from a large-scale corpus, informative samples are selected by an active learning approach.