DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Extracting the unextractable: a case study on verb-particles
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A general framework for distributional similarity
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discovering asymmetric entailment relations between verbs using selectional preferences
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Integrating pattern-based and distributional similarity methods for lexical entailment acquisition
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Discovery of event entailment knowledge from text corpora
Computer Speech and Language
Learning entailment rules for unary templates
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Unsupervised learning of narrative schemas and their participants
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Entity extraction via ensemble semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Weakly supervised learning of presupposition relations between verbs
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Directional distributional similarity for lexical inference
Natural Language Engineering
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
Learning entailment relations by global graph structure optimization
Computational Linguistics
| Hi-index | 0.00 |
Learning inference relations between verbs is at the heart of many semantic applications. However, most prior work on learning such rules focused on a rather narrow set of information sources: mainly distributional similarity, and to a lesser extent manually constructed verb co-occurrence patterns. In this paper, we claim that it is imperative to utilize information from various textual scopes: verb co-occurrence within a sentence, verb co-occurrence within a document, as well as overall corpus statistics. To this end, we propose a much richer novel set of linguistically motivated cues for detecting entailment between verbs and combine them as features in a supervised classification framework. We empirically demonstrate that our model significantly outperforms previous methods and that information from each textual scope contributes to the verb entailment learning task.