Discovery of inference rules for question-answering
Natural Language Engineering
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Modeling semantic containment and exclusion in natural language inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Learning entailment rules for unary templates
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The distributional similarity of sub-parses
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Context-theoretic semantics for natural language: an overview
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
"Ask not what textual entailment can do for you..."
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Global learning of focused entailment graphs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Directional distributional similarity for lexical inference
Natural Language Engineering
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Entailment detection systems are generally designed to work either on single words, relations or full sentences. We propose a new task -- detecting entailment between dependency graph fragments of any type -- which relaxes these restrictions and leads to much wider entailment discovery. An unsupervised framework is described that uses intrinsic similarity, multi-level extrinsic similarity and the detection of negation and hedged language to assign a confidence score to entailment relations between two fragments. The final system achieves 84.1% average precision on a data set of entailment examples from the biomedical domain.