RECESS: Resource for Extracting Cause, Effect, and Signal Spans

Tan, Fiona Anting, Hettiarachchi, Hansi, Hürriyetoğlu, Ali, Oostdijk, Nelleke, Caselli, Tommaso, Nomoto, Tadashi, Uca, Onur, Liza, Farhana Ferdousi ORCID: https://orcid.org/0000-0003-4854-5619 and Ng, See Kiong (2023) RECESS: Resource for Extracting Cause, Effect, and Signal Spans. In: Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 66-82.

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Abstract

Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal relations are fundamental to human decision making and reasoning, and extracting them from natural language texts is crucial for building effective natural language understanding models. However, the scarcity of annotated corpora for causal relations poses a challenge in the development of such tools. Thus, we created Resource for Extracting Cause, Effect, and Signal Spans (RECESS), a comprehensive corpus annotated for causality at different levels, including Cause, Effect, and Signal spans. The corpus contains 3,767 sentences, of which, 1,982 are causal sentences that contain a total of 2,754 causal relations. We report baseline experiments on two natural language tasks (Causal Sentence Classification, and Cause-Effect-Signal Span Detection), and establish initial benchmarks for future work. We conduct an in-depth analysis of the corpus and the properties of causal relations in text. RECESS is a valuable resource for developing and evaluating causal relation extraction models, benefiting researchers working on topics from information retrieval to natural language understanding and inference.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://aclanthology.org/2023.ijcnlp-mai...
Depositing User:	LivePure Connector
Date Deposited:	02 Oct 2024 10:30
Last Modified:	10 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/96858
DOI:	10.18653/v1/2023.ijcnlp-main.6

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