Discovery of inference rules for question-answering
Natural Language Engineering
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
Automatic paraphrase acquisition from news articles
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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
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
Lexical reference: a semantic matching subtask
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Automatic induction of FrameNet lexical units
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing entailment in intelligent tutoring systems*
Natural Language Engineering
Assessing the impact of frame semantics on textual entailment
Natural Language Engineering
A machine learning approach to textual entailment recognition
Natural Language Engineering
Classification errors in a domain-independent assessment system
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Multi-word expressions in textual inference: much ado about nothing?
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
SyMSS: A syntax-based measure for short-text semantic similarity
Data & Knowledge Engineering
Towards component-based textual entailment
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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
A lexical alignment model for probabilistic textual entailment
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Analysis of a textual entailer
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
DLSITE-1: lexical analysis for solving textual entailment recognition
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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In this paper we define two intermediate models of textual entailment, which correspond to lexical and lexical-syntactic levels of representation. We manually annotated a sample from the RTE dataset according to each model, compared the outcome for the two models, and explored how well they approximate the notion of entailment. We show that the lexical-syntactic model outperforms the lexical model, mainly due to a much lower rate of false-positives, but both models fail to achieve high recall. Our analysis also shows that paraphrases stand out as a dominant contributor to the entailment task. We suggest that our models and annotation methods can serve as an evaluation scheme for entailment at these levels.