TY - GEN
T1 - Semantic annotation for textual entailment recognition
AU - Toledo, Assaf
AU - Katrenko, Sophia
AU - Alexandropoulou, Stavroula
AU - Klockmann, Heidi
AU - Stern, Asher
AU - Dagan, Ido
AU - Winter, Yoad
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - RTE
KW - Semantic Annotation
KW - Textual Entailment
UR - http://www.scopus.com/inward/record.url?scp=84875820770&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37798-3_2
DO - 10.1007/978-3-642-37798-3_2
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AN - SCOPUS:84875820770
SN - 9783642377976
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 12
EP - 25
BT - Advances in Artificial Intelligence - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, Revised Selected Papers
T2 - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012
Y2 - 27 October 2012 through 4 November 2012
ER -