Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Learning probabilistic models of tree edit distance
Pattern Recognition
IEEE Transactions on Information Technology in Biomedicine
Towards Extensible Textual Entailment Engines: The EDITS Package
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Towards cross-lingual textual entailment
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
An open-source package for recognizing textual entailment
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Is it worth submitting this run?: assess your RTE system with a good sparring partner
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Efficient search for transformation-based inference
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Managing information disparity in multilingual document collections
ACM Transactions on Speech and Language Processing (TSLP)
DTD based costs for tree-edit distance in structured information retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Recently, there is a growing interest in working with tree-structured data in different applications and domains such as computational biology and natural language processing. Moreover, many applications in computational linguistics require the computation of similarities over pair of syntactic or semantic trees. In this context, Tree Edit Distance (TED) has been widely used for many years. However, one of the main constraints of this method is to tune the cost of edit operations, which makes it difficult or sometimes very challenging in dealing with complex problems. In this paper, we propose an original method to estimate and optimize the operation costs in TED, applying the Particle Swarm Optimization algorithm. Our experiments on Recognizing Textual Entailment show the success of this method in automatic estimation, rather than manual assignment of edit costs.