Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
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
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Japanese named entity extraction evaluation: analysis of results
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Chinese named entity identification using class-based language model
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Combining outputs of multiple Japanese named entity chunkers by stacking
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
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
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Chinese named entity recognition using lexicalized HMMs
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Factorizing complex models: a case study in mention detection
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Address standardization with latent semantic association
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mention detection crossing the language barrier
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Language specific issue and feature exploration in Chinese event extraction
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Morphology-Based Segmentation Combination for Arabic Mention Detection
ACM Transactions on Asian Language Information Processing (TALIP)
Cross-Language Information Propagation for Arabic Mention Detection
ACM Transactions on Asian Language Information Processing (TALIP)
Arabic Mention Detection: toward better unit of analysis
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using deep belief nets for Chinese named entity categorization
NEWS '10 Proceedings of the 2010 Named Entities Workshop
EagleEye: entity-centric business intelligence for smarter decisions
IBM Journal of Research and Development
Chinese named entity recognition based on multilevel linguistic features
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique answer to this question, we discuss in detail advantages and disadvantages of each model, identify problems in segmentation and suggest possible solutions, presenting our observations, analysis, and experimental results. The second topic of this paper is classifier combination. We present and describe four classifiers for Chinese named entity recognition and describe various methods for combining their outputs. The results demonstrate that classifier combination is an effective technique of improving system performance: experiments over a large annotated corpus of fine-grained entity types exhibit a 10% relative reduction in F-measure error.