An Efficient Digital Search Algorithm by Using a Double-Array Structure
IEEE Transactions on Software Engineering
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Evaluation and extension of maximum entropy models with inequality constraints
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Linear-time dependency analysis for Japanese
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Effective self-training for parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
The VLDB Journal — The International Journal on Very Large Data Bases
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
Space-efficient static trees and graphs
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
An approximate approach for training polynomial kernel SVMs in linear time
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Japanese dependency parsing using a tournament model
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Exploring domain differences for the design of pronoun resolution systems for biomedical text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semi-supervised training for the averaged perceptron POS tagger
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Learning combination features with L1 regularization
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Engineering the LOUDS succinct tree representation
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Identifying constant and unique relations by using time-series text
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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This paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. The key idea is to precompute as partial results the weights of primitive feature vectors that appear frequently in the target NLP task. A trie compactly stores the primitive feature vectors with their weights, and it enables the classifier to find for a given feature vector its longest prefix feature vector whose weight has already been computed. Experimental results for a Japanese dependency parsing task show that our method speeded up the svm and llm classifiers of the parsers, which achieved accuracy of 90.84/90.71%, by a factor of 10.7/11.6.