The paper “Annotating causality in the TempEval-3 corpus” by Paramita Mirza, Rachele Sprugnoli, Sara Tonelli and Manuela Speranza has been accepted for presentation at EACL-2014 Workshop on Computational Approaches to Causality in Language, 26 April, Gothenburg, Sweden.


“While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen’s temporal logics, the question on how to annotate other types of event relations, in particular causal ones, is still open. In this work, we present some annotation guidelines to capture causality between event pairs, partly inspired by TimeML. We then implement a rule-based algorithm that, using the instructions given in the guidelines, tries to automatically identify explicit causal relations in the TempEval-3 corpus. This annotation is then used as a basis to compute some statistics on the behavior of causal cues in text, and to perform a preliminary investigation on the interaction between causal and temporal relations.”