Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Building a large-scale annotated Chinese corpus
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The first international Chinese word segmentation Bakeoff
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Combining segmenter and chunker for Chinese word segmentation
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Chinese word segmentation as LMR tagging
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
A maximum entropy Chinese character-based parser
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Adaptive Chinese word segmentation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Composition of conditional random fields for transfer learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Joint parsing and semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
The integration of syntactic parsing and semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Optimizing Chinese word segmentation for machine translation performance
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
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
Joint training and decoding using virtual nodes for cascaded segmentation and tagging tasks
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
Syntactic processing using the generalized perceptron and beam search
Computational Linguistics
Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation
ACM Transactions on Asian Language Information Processing (TALIP)
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
Named entity recognition and identification for finding the owner of a home page
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Many problems in NLP require solving a cascade of subtasks. Traditional pipeline approaches yield to error propagation and prohibit joint training/ decoding between subtasks. Existing solutions to this problem do not guarantee nonviolation of hard-constraints imposed by subtasks and thus give rise to inconsistent results, especially in cases where segmentation task precedes labeling task. We present a method that performs joint decoding of separately trained Conditional Random Field (CRF) models, while guarding against violations of hard-constraints. Evaluated on Chinese word segmentation and part-of-speech (POS) tagging tasks, our proposed method achieved state-of-the-art performance on both the Penn Chinese Treebank and First SIGHAN Bakeoff datasets. On both segmentation and POS tagging tasks, the proposed method consistently improves over baseline methods that do not perform joint decoding.