A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A memory-based approach to learning shallow natural language patterns
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A computational model of language performance: Data Oriented Parsing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Machine learning-based named entity recognition via effective integration of various evidences
Natural Language Engineering
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Improving pronoun resolution using statistics-based semantic compatibility information
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An empirical study of Chinese chunking
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Frequent words' grammar information in Chinese chunking
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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This paper proposes an error-driven HMM-based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to decrease the memory requirement by keeping only positive lexical entries and makes it possible to further incorporate more context-dependent lexical entries. Finally, memory-based learning is adopted to further improve the performance of the chunk tagger.