Machine Learning
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
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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
Committee-based decision making in probabilistic partial parsing
COLING '00 Proceedings of the 18th 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
Extracting word sequence correspondences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Extracting important sentences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A stochastic parser based on an SLM with arboreal context trees
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An empirical study of active learning with support vector machines for Japanese word segmentation
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
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
Learning with multiple stacking for named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
Linear-time dependency analysis for Japanese
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Dependency structure analysis and sentence boundary detection in spontaneous Japanese
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Stochastic discourse modeling in spoken dialogue systems using semantic dependency graphs
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Japanese dependency analysis based on improved SVM and KNN
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Determining the Dependency Among Clauses Based on Machine Learning Techniques
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Chinese deterministic dependency analysis with consideration of long-distance dependency
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Clause boundary recognition using support vector machines
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
The word is mightier than the count: accumulating translation resources from parsed parallel corpora
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Ontology based knowledge extraction for shipyard fabrication workshop reports
Expert Systems with Applications: An International Journal
Dependency parsing and projection based on word-pair classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
SVM-based clause-dependency determination in syntactic analysis
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Comparison of various machine learning-based classifications of relative clauses
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Japanese dependency analysis based on parallel relation
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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
Combine constituent and dependency parsing via reranking
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents a method of Japanese dependency structure analysis based on Support Vector Machines (SVMs). Conventional parsing techniques based on Machine Learning framework, such as Decision Trees and Maximum Entropy Models, have difficulty in selecting useful features as well as finding appropriate combination of selected features. On the other hand, it is well-known that SVMs achieve high generalization performance even with input data of very high dimensional feature space. Furthermore, by introducing the Kernel principle, SVMs can carry out the training in high-dimensional spaces with a smaller computational cost independent of their dimensionality. We apply SVMs to Japanese dependency structure identification problem. Experimental results on Kyoto University corpus show that our system achieves the accuracy of 89.09% even with small training data (7958 sentences).