Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
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An improved boosting algorithm and its application to text categorization
Proceedings of the ninth international conference on Information and knowledge management
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Computing Optimal Hypotheses Efficiently for Boosting
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Text chunking using regularized Winnow
ACL '01 Proceedings of the 39th 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
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
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Can twitter be an alternative of real-world sensors?
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
A named entity extraction using word information repeatedly collected from unlabeled data
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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Combination of features contributes to a significant improvement in accuracy on tasks such as part-of-speech (POS) tagging and text chunking, compared with using atomic features. However, selecting combination of features on learning with large-scale and feature-rich training data requires long training time. We propose a fast boosting-based algorithm for learning rules represented by combination of features. Our algorithm constructs a set of rules by repeating the process to select several rules from a small proportion of candidate rules. The candidate rules are generated from a subset of all the features with a technique similar to beam search. Then we propose POS tagging and text chunking based on our learning algorithm. Our tagger and chunker use candidate POS tags or chunk tags of each word collected from automatically tagged data. We evaluate our methods with English POS tagging and text chunking. The experimental results show that the training time of our algorithm are about 50 times faster than Support Vector Machines with polynomial kernel on the average while maintaining state-of-the-art accuracy and faster classification speed.