WordNet: a lexical database for English
Communications of the ACM
Induction of semantic classes from natural language text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
What syntax can contribute in the entailment task
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
A syntactic textual entailment system based on dependency parser
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
A rule-based human interpretation system for semantic textual similarity task
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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Recent research suggests that sentence structure can improve the accuracy of recognizing textual entailments and paraphrasing. Although background knowledge such as gazetteers, WordNet and custom built knowledge bases are also likely to improve performance, our goal in this paper is to characterize the syntactic features alone that aid in accurate entailment prediction. We describe candidate features, the role of machine learning, and two final decision rules. These rules resulted in an accuracy of 60.50 and 65.87% and average precision of 58.97 and 60.96% in RTE3Test and suggest that sentence structure alone can improve entailment accuracy by 9.25 to 14.62% over the baseline majority class.