The nature of statistical learning theory
The nature of statistical learning theory
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Text classification using string kernels
The Journal of Machine Learning Research
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Kernel methods for predicting protein--protein interactions
Bioinformatics
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
On Pairwise Kernels: An Efficient Alternative and Generalization Analysis
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Modeling semantic containment and exclusion in natural language inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Paraphrase recognition via dissimilarity significance classification
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Shallow semantics in fast textual entailment rule learners
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Paraphrase identification as probabilistic quasi-synchronous recognition
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Tree edit models for recognizing textual entailments, paraphrases, and answers to questions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An introduction to string re-writing kernel
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referred to as string re-writing kernel, to address the problem. A string re-writing kernel measures the similarity between two pairs of strings, each pair representing re-writing of a string. It can capture the lexical and structural similarity between two pairs of sentences without the need of constructing syntactic trees. We further propose an instance of string re-writing kernel which can be computed efficiently. Experimental results on benchmark datasets show that our method can achieve better results than state-of-the-art methods on two sentence re-writing learning tasks: paraphrase identification and recognizing textual entailment.