Self-organized language modeling for speech recognition
Readings in speech recognition
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Information Retrieval
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
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
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
Distributional representations for handling sparsity in supervised sequence-labeling
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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 (DHMM) with long state dependence (LSD-DHMM) to segment and label sequential data. The LSD-DHMM overcomes the strong context independent assumption in traditional generative HMMs (GHMMs) and models the sequential data in a discriminative way, by assuming a novel mutual information independence. As a result, the LSD-DHMM separately models the long state dependence in its state transition model and the observation dependence in its output model. In this paper, a variable-length mutual information-based modeling approach and an ensemble of kNN probability estimators are proposed to capture the long state dependence and the observation dependence respectively. The evaluation on shallow parsing shows that the LSD-DHMM not only significantly outperforms GHMMs but also much outperforms other DHMMs. This suggests that the LSD-DHMM can effectively capture the long context dependence to segment and label sequential data.