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
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Flexible text segmentation with structured multilabel classification
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Latent variable perceptron algorithm for structured classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
A cost sensitive part-of-speech tagging: differentiating serious errors from minor errors
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Exploiting chunk-level features to improve phrase chunking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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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Shallow parsing is one of many NLP tasks that can be reduced to a sequence labeling problem. In this paper we show that the latent-dynamics (i.e., hidden substructure of shallow phrases) constitutes a problem in shallow parsing, and we show that modeling this intermediate structure is useful. By analyzing the automatically learned hidden states, we show how the latent conditional model explicitly learn latent-dynamics. We propose in this paper the Best Label Path (BLP) inference algorithm, which is able to produce the most probable label sequence on latent conditional models. It outperforms two existing inference algorithms. With the BLP inference, the LDCRF model significantly outperforms CRF models on word features, and achieves comparable performance of the most successful shallow parsers on the CoNLL data when further using part-of-speech features.