Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A discriminative matching approach to word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Negation, contrast and contradiction in text processing
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A probabilistic setting and lexical cooccurrence model for textual entailment
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Recognizing textual entailment: is word similarity enough?
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Answering questions with authority
Proceedings of the 17th ACM conference on Information and knowledge management
Addressing ontology-based question answering with collections of user queries
Information Processing and Management: an International Journal
AORTE for Recognizing Textual Entailment
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A phrase-based alignment model for natural language inference
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Inferring textual entailment with a probabilistically sound calculus*
Natural Language Engineering
Recognizing entailment in intelligent tutoring systems*
Natural Language Engineering
Assessing the impact of frame semantics on textual entailment
Natural Language Engineering
A framework for entailed relation recognition
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Multi-word expressions in textual inference: much ado about nothing?
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
Layer structures and conceptual hierarchies in semantic representations for NLP
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Automatic patient search for breast cancer clinical trials using free-text medical reports
Proceedings of the 1st ACM International Health Informatics Symposium
Is it worth submitting this run?: assess your RTE system with a good sparring partner
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Text content reliability estimation in web documents: a new proposal
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Modality and negation: An introduction to the special issue
Computational Linguistics
Are you sure that this happened? assessing the factuality degree of events in text
Computational Linguistics
Semantic annotation for textual entailment recognition
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
An investigation into the application of ensemble learning for entailment classification
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Promising results were achieved: our system correctly identified more than 80% of examples from the RTE-3 Test Set correctly, without the need for additional sources of training data or other web-based resources.