The nature of statistical learning theory
The nature of statistical learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Slot Grammar: A System for Simpler Construction of Practical Natural Language Grammars
Proceedings of the International Symposium on Natural Language and Logic
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Kernel methods for relation extraction
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
Exploiting unannotated corpora for tagging and chunking
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Evita: a robust event recognizer for QA systems
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Tree kernels for semantic role labeling
Computational Linguistics
Identification of event mentions and their semantic class
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
TimeML events recognition and classification: learning CRF models with semantic roles
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Special issue on statistical learning of natural language structured input and output
Natural Language Engineering
Question analysis: how watson reads a clue
IBM Journal of Research and Development
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State-of-the-art approaches to token labeling within text documents typically cast the problem either as a classification task, without using complex structural characteristics of the input, or as a sequential labeling task, carried out by a Conditional Random Field (CRF) classifier. Here we explore principled ways for structure to be brought to bear on the task. In line with recent trends in statistical learning of structured natural language input, we use a Support Vector Machine (SVM) classification framework deploying tree kernels. We then propose tree transformations and decorations, as a methodology for modeling complex linguistic phenomena in highly multi-dimensional feature spaces. We develop a general purpose tree engineering framework, which enables us to transcend the typically complex and laborious process of feature engineering. We build kernel based classifiers for two token labeling tasks: fine-grained event recognition, and lexical answer type detection in questions. For both, we show that in comparison with a corresponding linear kernel SVM, our method of using tree kernels improves recognition, thanks to appropriately engineering tree structures for use by the tree kernel. We also observe significant improvements when comparing with a CRF-based realization of structured prediction, itself performing at levels comparable to state-of-the-art.