Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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
Applying conditional random fields to chinese shallow parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Part-of-speech (POS) tagging and shallow parsing are sequence modeling problems. While HMM and other generative models are not the most appropriate for the task of labeling sequential data. Compared with HMM, Maximum Entropy Markov models (MEMM) and other discriminative finite-state models can easily fused more features, however they suffer from the label bias problem. This paper presents a method of Chinese POS tagging and shallow parsing based on conditional random fields (CRF), as new discriminative sequential models, which may incorporate many rich features and well avoid the label bias problem. Moreover, we propose the information feedback from syntactical analysis to lexical analysis, since natural language should be a multi-knowledge interaction in nature. Experiments show that CRF approach achieves 0.70% F-score improvement in POS tagging and 0.67% improvement in shallow parsing. And we also confirm the effectiveness of information feedback to some complicated multi-class words.