Making large-scale support vector machine learning practical
Advances in kernel methods
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
Support Vector Learning for Semantic Argument Classification
Machine Learning
An SVM based voting algorithm with application to parse reranking
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning question classifiers: the role of semantic information
Natural Language Engineering
Exploring syntactic features for relation extraction using a convolution tree kernel
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Efficient linearization of tree kernel functions
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Sparse kernel SVMs via cutting-plane training
Machine Learning
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
The Journal of Machine Learning Research
Fast support vector machines for structural Kernels
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Structural relationships for large-scale learning of answer re-ranking
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we present an extensive study of the cutting-plane algorithm (CPA) applied to structural kernels for advanced text classification on large datasets. In particular, we carry out a comprehensive experimentation on two interesting natural language tasks, e.g. predicate argument extraction and question answering. Our results show that (i) CPA applied to train a non-linear model with different tree kernels fully matches the accuracy of the conventional SVM algorithm while being ten times faster; (ii) by using smaller sampling sizes to approximate subgradients in CPA we can trade off accuracy for speed, yet the optimal parameters and kernels found remain optimal for the exact SVM. These results open numerous research perspectives, e.g. in natural language processing, as they show that complex structural kernels can be efficiently used in real-world applications. For example, for the first time, we could carry out extensive tests of several tree kernels on millions of training instances. As a direct benefit, we could experiment with a variant of the partial tree kernel, which we also propose in this paper.