The nature of statistical learning theory
The nature of statistical learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Introduction to special issue on machine learning approaches to shallow parsing
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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
Text chunking by combining hand-crafted rules and memory-based learning
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - 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
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Statistics based hybrid approach to Chinese base phrase identification
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
SIGHAN '02 Proceedings of the first SIGHAN workshop on Chinese language processing - Volume 18
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Chinese chunk identification using SVMs plus sigmoid
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Applying conditional random fields to chinese shallow parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
An empirical study of Vietnamese noun phrase chunking with discriminative sequence models
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Chinese semantic role labeling with shallow parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Semantics-driven shallow parsing for Chinese semantic role labeling
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
A minimum error weighting combination strategy for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Frequent words' grammar information in Chinese chunking
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Chinese chunking with tri-training learning
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
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
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In this paper, we describe an empirical study of Chinese chunking on a corpus, which is extracted from UPENN Chinese Treebank-4 (CTB4). First, we compare the performance of the state-of-the-art machine learning models. Then we propose two approaches in order to improve the performance of Chinese chunking. 1) We propose an approach to resolve the special problems of Chinese chunking. This approach extends the chunk tags for every problem by a tag-extension function. 2) We propose two novel voting methods based on the characteristics of chunking task. Compared with traditional voting methods, the proposed voting methods consider long distance information. The experimental results show that the SVMs model outperforms the other models and that our proposed approaches can improve performance significantly.