A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-based n-gram models of natural language
Computational Linguistics
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Toward a unified approach to statistical language modeling for Chinese
ACM Transactions on Asian Language Information Processing (TALIP)
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Improved source-channel models for Chinese word segmentation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Chinese named entity recognition using lexicalized HMMs
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
Chinese lexical analysis using hierarchical hidden Markov model
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Single character Chinese named entity recognition
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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
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
ACM Transactions on Asian Language Information Processing (TALIP)
Integration of Named Entity Information for Chinese Word Segmentation Based on Maximum Entropy
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Recognize person names from Chinese texts based on clustering SVM
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Automatic Expansion of Chinese Abbreviations by Web Mining
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
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
Chinese named entity recognition with a hybrid-statistical model
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Chinese abbreviation identification using abbreviation-template features and context information
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Chinese named entity recognition based on multilevel linguistic features
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Integrating punctuation rules and naïve bayesian model for chinese creation title recognition
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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We consider here the problem of Chinese named entity (NE) identification using statistical language model(LM). In this research, word segmentation and NE identification have been integrated into a unified framework that consists of several class-based language models. We also adopt a hierarchical structure for one of the LMs so that the nested entities in organization names can be identified. The evaluation on a large test set shows consistent improvements. Our experiments further demonstrate the improvement after seamlessly integrating with linguistic heuristic information, cache-based model and NE abbreviation identification.