The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Chinese word segmentation as LMR tagging
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Adaptive Chinese word segmentation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Unsupervised search-based structured prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A hybrid approach to word segmentation and POS tagging
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Comparing the accuracy of CCG and Penn Treebank parsers
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling
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
An error-driven word-character hybrid model for joint Chinese word segmentation and POS tagging
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
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 fast decoder for joint word segmentation and POS-tagging using a single discriminative model
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A character-based joint model for Chinese word segmentation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A stacked sub-word model for joint Chinese word segmentation and part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Parsing the internal structure of words: a new paradigm for Chinese word segmentation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Fully unsupervised word segmentation with BVE and MDL
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Better automatic treebank conversion using a feature-based approach
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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In this paper we first describe the technology of automatic annotation transformation, which is based on the annotation adaptation algorithm (Jiang et al., 2009). It can automatically transform a human-annotated corpus from one annotation guideline to another. We then propose two optimization strategies, iterative training and predict-self reestimation, to further improve the accuracy of annotation guideline transformation. Experiments on Chinese word segmentation show that, the iterative training strategy together with predict-self reestimation brings significant improvement over the simple annotation transformation baseline, and leads to classifiers with significantly higher accuracy and several times faster processing than annotation adaptation does. On the Penn Chinese Treebank 5.0, it achieves an F-measure of 98.43%, significantly outperforms previous works although using a single classifier with only local features.