Induction of semantic classes from natural language text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
A discourse commitment-based framework for recognizing textual entailment
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
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
Discriminative learning over constrained latent representations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Content-driven trust propagation framework
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Gauging the internet doctor: ranking medical claims based on community knowledge
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
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We define the problem of recognizing entailed relations -- given an open set of relations, find all occurrences of the relations of interest in a given document set -- and pose it as a challenge to scalable information extraction and retrieval. Existing approaches to relation recognition do not address well problems with an open set of relations and a need for high recall: supervised methods are not easily scaled, while unsupervised and semi-supervised methods address a limited aspect of the problem, as they are restricted to frequent, explicit, highly localized patterns. We argue that textual entailment (TE) is necessary to solve such problems, propose a scalable TE architecture, and provide preliminary results on an Entailed Relation Recognition task.