In this work we present an annotation framework to capture causality between events, inspired by TimeML, and a language resource covering both temporal and causal relations. This data set is then used to build an automatic extraction system for causal signals and causal links between given event pairs. The evaluation and analysis of the system's performance provides an insight into explicit causality in text and the connection between temporal and causal relations.
Recently, REF presented annotation guidelines and annotated explicit causality between events in Timebank.
An Analysis of Causality between Events and its Relation to Temporal Information
COLING
Computer Science