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
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Lexicalized hidden Markov models for part-of-speech tagging
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Improving part-of-speech tagging using lexicalized HMMs
Natural Language Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Revision learning and its application to part-of-speech tagging
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Named entity recognition with character-level models
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 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 multilevel linguistic features
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Using a smoothing maximum entropy model for chinese nominal entity tagging
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Chinese unknown word identification using class-based LM
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Chinese word segmentation as morpheme-based lexical chunking
Information Sciences: an International Journal
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
OpinionMiner: a novel machine learning system for web opinion mining and extraction
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
A unified framework for text analysis in chinese TTS
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
International Journal of Information Retrieval Research
Journal of Biomedical Informatics
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This paper presents a lexicalized HMM-based approach to Chinese named entity recognition (NER). To tackle the problem of unknown words, we unify unknown word identification and NER as a single tagging task on a sequence of known words. To do this, we first employ a known-word bigram-based model to segment a sentence into a sequence of known words, and then apply the uniformly lexicalized HMMs to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity. Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. We have tested our system using different public corpora. The results show that lexicalized HMMs can substantially improve NER performance over standard HMMs. The results also indicate that character-based tagging (viz. the tagging based on pure single-character words) is comparable to and can even outperform the relevant known-word based tagging when a lexicalization technique is applied.