Elements of information theory
Elements of information theory
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Computational Linguistics
Nonstationary hidden Markov model
Signal Processing
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
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
Tutorial on maximum likelihood estimation
Journal of Mathematical Psychology
Hidden Markov models with states depending on observations
Pattern Recognition Letters
Discriminative syntactic language modeling for speech recognition
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Minimum sample risk methods for language modeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Smoothing methods in maximum entropy language modeling
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Principles of non-stationary hidden markov model and its applications to sequence labeling task
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
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Sequence labeling is a core task in natural language processing. The maximum entropy Markov model (MEMM) is a powerful tool in performing this task. This article enhances the traditional MEMM by exploiting the positional information of language elements. The stationary hypothesis is relaxed in MEMM, and the nonstationary MEMM (NS-MEMM) is proposed. Several related issues are discussed in detail, including the representation of positional information, NS-MEMM implementation, smoothing techniques, and the space complexity issue. Furthermore, the asymmetric NS-MEMM presents a more flexible way to exploit positional information. In the experiments, NS-MEMM is evaluated on both the Chinese and the English pos-tagging tasks. According to the experimental results, NS-MEMM yields effective improvements over MEMM by exploiting positional information. The smoothing techniques in this article effectively solve the NS-MEMM data-sparseness problem; the asymmetric NS-MEMM is also an improvement by exploiting positional information in a more flexible way.