WordNet: a lexical database for English
Communications of the ACM
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A categorial variation database for English
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Hypothesis transformation and semantic variability rules used in recognizing textual entailment
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Semantic inference at the lexical-syntactic level for textual entailment recognition
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
A discourse commitment-based framework for recognizing textual entailment
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
"Ask not what textual entailment can do for you..."
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Text Processing with GATE
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
VENSES – a linguistically-based system for semantic evaluation
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Hi-index | 0.00 |
We introduce a new semantic annotation scheme for the Recognizing Textual Entailment (RTE) dataset as well as a manually annotated dataset that uses this scheme. The scheme addresses three types of modification that license entailment patterns: restrictive, appositive and conjunctive, with a formal semantic specification of these patterns' contribution for establishing entailment. These inferential constructions were found to occur in 77.68% of the entailments in the RTE 1-3 corpora. They were annotated with cross-annotator agreement of 70.73% on average. A central aim of our annotations is to examine components that address these phenomena in RTE systems. Specifically, the new annotated dataset is used for examining a syntactic rule base within the BIUTEE recognizer, a publicly available entailment system. According to our tests, the rule base is rarely used to process the phenomena annotated in our corpus and most of the recognition work is done by other components in the system.