Automatic labeling of semantic roles
Computational Linguistics
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Combined SVM-Based Feature Selection and Classification
Machine Learning
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Fast On-line Kernel Learning for Trees
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd 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
Speeding up training with tree kernels for node relation labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Tree kernels for semantic role labeling
Computational Linguistics
Porting statistical parsers with data-defined kernels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Reverse engineering of tree kernel feature spaces
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
On reverse feature engineering of syntactic tree kernels
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Large-scale support vector learning with structural kernels
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
State-of-the-art kernels for natural language processing
ACL '12 Tutorial Abstracts of ACL 2012
Verb classification using distributional similarity in syntactic and semantic structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Fast linearization of tree kernels over large-scale data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two major drawbacks: first, the implicit representation of feature spaces does not allow us to understand which features actually triggered the generalization; second, the resulting computational burden may in some cases render unfeasible to use large data sets for training. We propose an approach based on feature space reverse engineering to tackle both problems. Our experiments with Tree Kernels on a Semantic Role Labeling data set show that the proposed approach can drastically reduce the computational footprint while yielding almost unaffected accuracy.