Semantic annotation for textual entailment recognition

Assaf Toledo, Sophia Katrenko, Stavroula Alexandropoulou, Heidi Klockmann, Asher Stern, Ido Dagan, Yoad Winter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, Revised Selected Papers
Number of pages14
EditionPART 2
StatePublished - 2013
Event11th Mexican International Conference on Artificial Intelligence, MICAI 2012 - San Luis Potosi, Mexico
Duration: 27 Oct 20124 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7630 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Mexican International Conference on Artificial Intelligence, MICAI 2012
CitySan Luis Potosi


  • RTE
  • Semantic Annotation
  • Textual Entailment


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