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
A compression-based algorithm for Chinese word segmentation
Computational Linguistics
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chinese named entity recognition using lexicalized HMMs
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Chinese named entity recognition based on multiple features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Semi-joint labeling for chinese named entity recognition
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Chinese named entity recognition based on hierarchical hybrid model
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Information Processing and Management: an International Journal
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AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
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Named Entity Recognition is one of the key techniques in the fields of natural language processing, information retrieval, question answering and so on. Unfortunately, Chinese Named Entity Recognition (NER) is more difficult for the lack of capitalization information and the uncertainty in word segmentation. In this paper, we present a hybrid algorithm which can combine a class-based statistical model with various types of human knowledge very well. In order to avoid data sparseness problem, we employ a back-off model and [Abstract contained text which could not be captured.], a Chinese thesaurus, to smooth the parameters in the model. The F-measure of person names, location names, and organization names on the newswire test data for the 1999 IEER evaluation in Mandarin is 86.84%, 84.40% and 76.22% respectively.