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
Tagging English text with a probabilistic model
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Error-driven HMM-based chunk tagger with context-dependent lexicon
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Short note on two output-dependent hidden Markov models
Pattern Recognition Letters
Elementary discourse unit in chinese discourse structure analysis
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
A chinese sentence segmentation approach based on comma
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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This paper proposes a discriminative HMM to directly model output context dependence. The discriminative HMM assumes mutual information independence in its output model that a ''hidden'' state is only dependent on the outputs and independent on other ''hidden'' states. As a result, it overcomes the output context independent assumption in the traditional generative HMM. In addition, a dynamic back-off modelling algorithm using constraint relaxation principle is proposed to resolve the data sparseness problem in the discriminative HMM due to the direct modelling of the output context dependence in its output model. The evaluations on part-of-speech tagging and phrase chunking show that the discriminative HMM can effectively capture the output context dependence through its output context dependent output model and the dynamic back-off modelling algorithm.