Original Contribution: Stacked generalization
Neural Networks
Computational Linguistics
The nature of statistical learning theory
The nature of statistical learning theory
Japanese dependency structure analysis based on maximum entropy models
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Using decision trees to construct a practical parser
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Backward beam search algorithm for dependency analysis of Japanese
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Japanese dependency analysis using a deterministic finite state transducer
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Japanese dependency parsing using co-occurrence information and a combination of case elements
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Analysis of selective strategies to build a dependency-analyzed corpus
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
An Assistant Interface for Finding Query-Related Proper Nouns
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Japanese dependency parsing using a tournament model
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Coordination disambiguation without any similarities
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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
A unified single scan algorithm for Japanese base phrase chunking and dependency parsing
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Polynomial to linear: efficient classification with conjunctive features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Using smaller constituents rather than sentences in active learning for Japanese dependency parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Using a partially annotated corpus to build a dependency parser for japanese
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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We present a novel algorithm for Japanese dependency analysis. The algorithm allows us to analyze dependency structures of a sentence in linear-time while keeping a state-of-the-art accuracy. In this paper, we show a formal description of the algorithm and discuss it theoretically with respect to time complexity. In addition, we evaluate its efficiency and performance empirically against the Kyoto University Corpus. The proposed algorithm with improved models for dependency yields the best accuracy in the previously published results on the Kyoto University Corpus.