Self-organized language modeling for speech recognition
Readings in speech recognition
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Part of speech tagging using a network of linear separators
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Named entity recognition: a maximum entropy approach using global information
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
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
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This paper introduces a Mutual Information Independence Model (MIIM) and proposes a feature relaxation principle to resolve the data sparseness problem in MIIM-based named entity recognition via hierarchical features. In this way, a named entity recognition system with better performance and better portability can be achieved. Evaluation of our system on MUC-6 and MUC-7 English named entity tasks achieves F-measures of 96.1% and 93.7% respectively. It also shows that 20K words of training data would have given the performance of 90 percent with the hierarchical structure in the features compared with 30K words without the hierarchical structure in the features. This suggests that the hierarchical features provide a potential for much better portability.