The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
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
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Semantic roles for SMT: a hybrid two-pass model
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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Recently, many researches in natural language learning have considered the representation of complex linguistic phenomena by means of structural kernels. In particular, tree kernels have been used to represent verbal subcategorization frame (SCF) information for predicate argument classification. As the SCF is a relevant clue to learn the relation between syntax and semantic, the classification algorithm accuracy was remarkable enhanced. In this article, we extend such work by studying the impact of the SCF tree kernel on both PropBank and FrameNet semantic roles. The experiments with Support Vector Machines (SVMs) confirm a strong link between the SCF and the semantics of the verbal predicates as well as the benefit of using kernels in diverse and complex test conditions, e.g. classification of unseen verbs.