A maximum entropy approach to natural language processing
Computational Linguistics
Self-Supervised Chinese Word Segmentation
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Chinese word segmentation without using lexicon and hand-crafted training data
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Accessor variety criteria for Chinese word extraction
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
Some applications of tree-based modelling to speech and language
HLT '89 Proceedings of the workshop on Speech and Natural Language
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
Contextual dependencies in unsupervised word segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Chinese segmentation and new word detection using conditional random fields
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Unsupervised segmentation of Chinese text by use of branching entropy
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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
Punctuation: making a point in unsupervised dependency parsing
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Non-parametric bayesian segmentation of Japanese noun phrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Enhancing Chinese word segmentation using unlabeled data
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation
ACM Transactions on Asian Language Information Processing (TALIP)
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We present a Chinese word segmentation model learned from punctuation marks which are perfect word delimiters. The learning is aided by a manually segmented corpus. Our method is considerably more effective than previous methods in unknown word recognition. This is a step toward addressing one of the toughest problems in Chinese word segmentation.