This paper presents CATS, a causal and temporal annotation scheme designed to jointly represent causal relations and temporal structures in news texts. The proposed framework integrates components of ISO 24617 Semantic Annotation Framework (SemAF), drawing in particular on Part 1 (Time and Events) (ISO 24617-1: 2012iso24617) and Part 8 (Semantic Relations in Discourse) (ISO 24617-8: 2016iso24617-8). Building on the Text2Story annotation framework (CITATION), the scheme adapts and extends its principles for representing temporal information while introducing new entities and links for modeling causal relations. The resulting annotation model enables the integrated representation of causal arguments, events, temporal relations, and causal signals within a unified structure. By jointly capturing causal and temporal dependencies, CATS provides a resource for studying the interaction between causality and temporality in discourse and supports downstream NLP tasks such as event extraction, temporal ordering, and causal reasoning.

High-quality annotation is paramount for effective predictions of machine learning models. When the annotation is dense, achieving superior human labeling can be challenging since the most used annotation tools present an overloaded visualization of labels. Thus, we present a tool for viewing annotations made in corpora, specifically for temporal relations between events and temporal expressions, filling a gap in this type of tool. We focus on narrative text, which is a rich source for these types of elements.