DFKI-LT - Monolingual Social Media Datasets for Detecting Contradiction and Entailment

Piroska Lendvai, Isabelle Augenstein, Kalina Bontcheva, Thierry Declerck
Monolingual Social Media Datasets for Detecting Contradiction and Entailment
in: Nicoletta Calzolari (Conference Chair), Khalid Choukri (Conference Chair), Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asunción Moreno, Jan Odijk, Stelios Piperidis (eds.):
3 Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), Portoroz, Slovenia, ELRA, ELRA, 9, rue des Cordelières, 75013 Paris, 5/2016, 5/2016
 
Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined. We report on a new 3-way judgement Recognizing Textual Entailment (RTE) resource that originates in the Social Media domain, and explain our semi-automatic creation method for the special purpose of information verification, which draws on manually established rumourous claims reported during crisis events. From about 500 English tweets related to 70 unique claims we compile and evaluate 5.4k RTE pairs, while continue automatizing the workflow to generate similar-sized datasets in other languages
 
Files: BibTeX, LREC_922_Paper_Final.pdf