WordNet: a lexical database for English
Communications of the ACM
Making large-scale support vector machine learning practical
Advances in kernel methods
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic learning of textual entailments with cross-pair similarities
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic role labeling via FrameNet, VerbNet and PropBank
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Recognising textual entailment with logical inference
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
An inference model for semantic entailment in natural language
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
A machine learning approach to textual entailment recognition
Natural Language Engineering
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In this paper, we provide a statistical machine learning representation of textual entailment via syntactic graphs constituted by tree pairs. We show that the natural way of representing the syntactic relations between text and hypothesis consists in the huge feature space of all possible syntactic tree fragment pairs, which can only be managed using kernel methods. Experiments with Support Vector Machines and our new kernels for paired trees show the validity of our interpretation.