Fast algorithms for the unit cost editing distance between trees
Journal of Algorithms
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discovery of inference rules for question-answering
Natural Language Engineering
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Textual entailment recognition based on dependency analysis and wordnet
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Dealing with Spoken Requests in a Multimodal Question Answering System
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Specialized entailment engines: approaching linguistic aspects of textual entailment
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Indexing for subtree similarity-search using edit distance
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Bricking Semantic Wikipedia by relation population and predicate suggestion
Web Intelligence and Agent Systems
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This paper addresses Textual Entailment (i.e. recognizing that the meaning of a text entails the meaning of another text) using a Tree Edit Distance algorithm between the syntactic trees of the two texts. A key aspect of the approach is the estimation of the cost for the editing operations (i.e. Insertion, Deletion, Substitution) among words. The aim of the paper is to compare the contribution of two different lexical resources for recognizing textual entailment: WordNet and a word-similarity database. In both cases we derive entailment rules that are used by the Tree Edit Distance Algorithm. We carried out a number of experiments over the PASCAL-RTE dataset in order to estimate the contribution of different combinations of the available resources.