met*: a method for discriminating metonymy and metaphor by computer
Computational Linguistics
Discovery of inference rules for question-answering
Natural Language Engineering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Can we derive general world knowledge from texts?
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Inference rules and their application to recognizing textual entailment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Combining word sense and usage for modeling frame semantics
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Textual entailment as an evaluation framework for metaphor resolution: a proposal
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Extracting paraphrase patterns from bilingual parallel corpora
Natural Language Engineering
Assessing the impact of frame semantics on textual entailment
Natural Language Engineering
Inference rules for recognizing textual entailment
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Towards component-based textual entailment
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Types of common-sense knowledge needed for recognizing textual entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning entailment relations by global graph structure optimization
Computational Linguistics
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
On the automatic generation of intermediate logic forms for wordnet glosses
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Leveraging Diverse Lexical Resources for Textual Entailment Recognition
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on RITE
Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet
Language Resources and Evaluation
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To score well in RTE3, and even more so to create good justifications for entailments, substantial lexical and world knowledge is needed. With this in mind, we present an analysis of a sample of the RTE3 positive entailment pairs, to identify where and what kinds of world knowledge are needed to fully identify and justify the entailment, and discuss several existing resources and their capacity for supplying that knowledge. We also briefly sketch the path we are following to build an RTE system (Our implementation is very preliminary, scoring 50.9% at the time of RTE). The contribution of this paper is thus a framework for discussing the knowledge requirements posed by RTE and some exploration of how these requirements can be met.