Papers › A Cognitive Approach to Annotating Causal Constructions in a Cross-Genre Corpus

A Cognitive Approach to Annotating Causal Constructions in a Cross-Genre Corpus

1 Jun 2022LREC (LAW) 2022 6archive 2025-07-28

Angela Cao, Gregor Williamson, Jinho D. Choi

We present a scheme for annotating causal language in various genres of text. Our annotation scheme is built on the popular categories of cause, enable, and prevent. These vague categories have many edge cases in natural language, and as such can prove difficult for annotators to consistently identify in practice. We introduce a decision based annotation method for handling these edge cases. We demonstrate that, by utilizing this method, annotators are able to achieve inter-annotator agreement which is comparable to that of previous studies. Furthermore, our method performs equally well across genres, highlighting the robustness of our annotation scheme. Finally, we observe notable variation in usage and frequency of causal language across different genres.

PaperPDFCode

Code

emorynlp/law-2022-causal officialmentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections