Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
Introduction to Algorithms
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
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
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Information Processing and Management: an International Journal
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Feature-rich statistical translation of noun phrases
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
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
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A robust multilingual portable phrase chunking system
Expert Systems with Applications: An International Journal
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Extracting named entities using support vector machines
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Efficient and robust phrase chunking using support vector machines
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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
A Probabilistic Graphical Model for Recognizing NP Chunks in Texts
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Edutainment '09 Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development
Discovering text patterns by a new graphic model
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Computers and Operations Research
Developing an algorithm for mining semantics in texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Robust twin support vector machine for pattern classification
Pattern Recognition
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
Integrating statistical and lexical information for recognizing textual entailments in text
Knowledge-Based Systems
Decision confidence-based multi-level support vector machines
Engineering Applications of Artificial Intelligence
Using robust dispersion estimation in support vector machines
Pattern Recognition
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Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, and noun phrase (NP)-chunking. Even though they yield very accurate results, they are not efficient for online applications, which need to handle hundreds of thousand words in a limited time. In this paper, we firstly re-examine five typical multiclass SVM methods and the adaptation to phrase chunking. However, most of them were inefficient when the number of phrase types scales. We thus introduce the proposed two new multiclass SVM models that make the system substantially faster in terms of training and testing while keeps the SVM accurate. The two methods can also be applied to similar tasks such as named entity recognition and Chinese word segmentation. Experiments on CoNLL-2000 chunking and Chinese base-chunking tasks showed that our method can achieve very competitive accuracy and at least 100 times faster than the state-of-the-art SVM-based phrase chunking method. Besides, the computational time complexity and the time cost analysis of our methods were also given in this paper.