Class-based n-gram models of natural language
Computational Linguistics
Machine Learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An efficient method for determining bilingual word classes
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fast, accurate deterministic parser for Chinese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Improving a simple bigram HMM part-of-speech tagger by latent annotation and self-training
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Transition-based parsing of the Chinese treebank using a global discriminative model
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Practical very large scale CRFs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Dynamic programming for linear-time incremental parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Word-based and character-based word segmentation models: comparison and combination
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhancing Chinese word segmentation using unlabeled data
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Joint models for Chinese POS tagging and dependency parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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From the perspective of structural linguistics, we explore paradigmatic and syntagmatic lexical relations for Chinese POS tagging, an important and challenging task for Chinese language processing. Paradigmatic lexical relations are explicitly captured by word clustering on large-scale unlabeled data and are used to design new features to enhance a discriminative tagger. Syntagmatic lexical relations are implicitly captured by constituent parsing and are utilized via system combination. Experiments on the Penn Chinese Treebank demonstrate the importance of both paradigmatic and syntagmatic relations. Our linguistically motivated approaches yield a relative error reduction of 18% in total over a state-of-the-art baseline.