C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to computational learning theory
An introduction to computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Statistical Language Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mistake-driven mixture of hierarchical tag context trees
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A portable & quick Japanese parser: QJP
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
IEEE Transactions on Information Theory
Feature selection in SVM text categorization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An integrated, dual learner for grammars and ontologies
Data & Knowledge Engineering
Text Categorization Using Transductive Boosting
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Boosting and Classification of Electronic Nose Data
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
An introduction to boosting and leveraging
Advanced lectures on machine learning
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
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This paper describes a novel and practical Japaneseparser that uses decision trees. First, we construct a single decision treeto estimate modification probabilities; how one phrase tends tomodify another. Next, we introduce a boosting algorithm in whichseveral decision trees are constructed and then combined forprobability estimation. The constructed parsers are evaluated usingthe EDR Japanese annotated corpus. The single-tree methodsignificantly outperforms the conventional Japanese stochasticmethods. Moreover, the boosted version of the parser is shown to have great advantages; (1) a better parsing accuracy than itssingle-tree counterpart for any amount of training data and (2) noover-fitting to data for various iterations. The presented parser,the first non-English stochastic parser with practical performance,should tighten the coupling between natural language processing andmachine learning.