The Causal News Corpus: Annotating causal relations in event sentences from news

Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk, Tadashi Nomoto, Hansi Hettiarachchi, Iqra Ameer, Onur Uca, Farhana Ferdousi Liza, Tiancheng Hu

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Abstract

Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that focus more on linguistics. Many guidelines restrict themselves to include only explicit relations or clause-based arguments. Therefore, we propose an annotation schema for event causality that addresses these concerns. We annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. Our corpus is known as the Causal News Corpus (CNC). A neural network built upon a state-of-the-art pre-trained language model performed well with 81.20% F1 score on test set, and 83.46% in 5-folds cross-validation. CNC is transferable across two external corpora: CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning. CNC also served as an effective training and pre-training dataset for the two external corpora. Lastly, we demonstrate the difficulty of our task to the layman in a crowd-sourced annotation exercise. Our annotated corpus is publicly available, providing a valuable resource for causal text mining researchers.
Original languageEnglish
Title of host publicationProceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)
PublisherEuropean Language Resources Association (ELRA)
Pages2298–2310
Number of pages13
Publication statusPublished - 2022
Event13th Conference on Language Resources and Evaluation - Marseille, France
Duration: 20 Jun 202225 Jun 2022
Conference number: 13

Conference

Conference13th Conference on Language Resources and Evaluation
Abbreviated titleLREC 2022
Country/TerritoryFrance
CityMarseille
Period20/06/2225/06/22

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