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Despite the importance of understanding causality, corpora addressing causalrelations are limited. There is a discrepancy between existing annotationguidelines of event causality and conventional causality corpora that focusmore on linguistics. Many guidelines restrict themselves to include onlyexplicit relations or clause-based arguments. Therefore, we propose anannotation schema for event causality that addresses these concerns. Weannotated 3,559 event sentences from protest event news with labels on whetherit contains causal relations or not. Our corpus is known as the Causal NewsCorpus (CNC). A neural network built upon a state-of-the-art pre-trainedlanguage model performed well with 81.20% F1 score on test set, and 83.46% in5-folds cross-validation. CNC is transferable across two external corpora:CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each ofthese external datasets for training, we achieved up to approximately 64% F1 onthe CNC test set without additional fine-tuning. CNC also served as aneffective training and pre-training dataset for the two external corpora.Lastly, we demonstrate the difficulty of our task to the layman in acrowd-sourced annotation exercise. Our annotated corpus is publicly available,providing a valuable resource for causal text mining researchers.

The Causal News Corpus: Annotating Causal Relations in Event Sentences from News