Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Minimum Bayes-Risk word alignments of bilingual texts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving the scalability of semi-Markov conditional random fields for named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Modeling latent-dynamic in shallow parsing: a latent conditional model with improved inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A discriminative latent variable chinese segmenter with hybrid word/character information
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Feature-rich translation by quasi-synchronous lattice parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Products of random latent variable grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A discriminative latent variable-based "DE" classifier for Chinese--English SMT
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Improved Chinese--English SMT with Chinese “DE” Construction Classification and Reordering
ACM Transactions on Asian Language Information Processing (TALIP)
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Unknown Chinese word extraction based on variety of overlapping strings
Information Processing and Management: an International Journal
Probabilistic Chinese word segmentation with non-local information and stochastic training
Information Processing and Management: an International Journal
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
ACM Transactions on Asian Language Information Processing (TALIP)
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Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming. Furthermore, we describe a straightforward solution on approximating the LDI, and show that the approximated LDI performs as well as the exact LDI, while the speed is much faster. Our experiments demonstrate that the proposed inference algorithm outperforms existing inference methods on a variety of natural language processing tasks.