The nature of statistical learning theory
The nature of statistical learning theory
Automatic labeling of semantic roles
Computational Linguistics
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Optimized Substructure Discovery for Semi-structured Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Convolution kernels with feature selection for natural language processing tasks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Tree kernels for semantic role labeling
Computational Linguistics
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Semantic role recognition using kernels on weighted marked ordered labeled trees
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Efficient linearization of tree kernel functions
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Syntactic and semantic kernels for short text pair categorization
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Syntactic Structural Kernels for Natural Language Interfaces to Databases
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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
Linguistic kernels for answer re-ranking in question answering systems
Information Processing and Management: an International Journal
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Using syntactic and semantic structural kernels for classifying definition questions in Jeopardy!
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semantic mapping between natural language questions and SQL queries via syntactic pairing
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
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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We present a method for speeding up the calculation of tree kernels during training. The calculation of tree kernels is still heavy even with efficient dynamic programming (DP) procedures. Our method maps trees into a small feature space where the inner product, which can be calculated much faster, yields the same value as the tree kernel for most tree pairs. The training is sped up by using the DP procedure only for the exceptional pairs. We describe an algorithm that detects such exceptional pairs and converts trees into vectors in a feature space. We propose tree kernels on marked labeled ordered trees and show that the training of SVMs for semantic role labeling using these kernels can be sped up by a factor of several tens.