Annotating causality in the TempEval-3 corpus

Abstract

While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen’s temporal logic, 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 to automatically identify explicit causal relations in the TempEval-3 corpus. Based on this annotation, we report some statistics on the behavior of causal cues in text and perform a preliminary investigation on the interaction between causal and temporal relations