Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Shallow parsing with pos taggers and linguistic features
The Journal of Machine Learning Research
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
On the parameter space of generative lexicalized statistical parsing models
On the parameter space of generative lexicalized statistical parsing models
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Flexible text segmentation with structured multilabel classification
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
An empirical study of Chinese chunking
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Modeling latent-dynamic in shallow parsing: a latent conditional model with improved inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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
Chinese chunk identification using SVMs plus sigmoid
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
A general and multi-lingual phrase chunking model based on masking method
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Applying conditional random fields to chinese shallow parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
| Hi-index | 0.00 |
Most existing systems solved the phrase chunking task with the sequence labeling approaches, in which the chunk candidates cannot be treated as a whole during parsing process so that the chunk-level features cannot be exploited in a natural way. In this paper, we formulate phrase chunking as a joint segmentation and labeling task. We propose an efficient dynamic programming algorithm with pruning for decoding, which allows the direct use of the features describing the internal characteristics of chunk and the features capturing the correlations between adjacent chunks. A relaxed, online maximum margin training algorithm is used for learning. Within this framework, we explored a variety of effective feature representations for Chinese phrase chunking. The experimental results show that the use of chunk-level features can lead to significant performance improvement, and that our approach achieves state-of-the-art performance. In particular, our approach is much better at recognizing long and complicated phrases.