Countercausal News Corpus (CCNC)

Intra-sentence Polarity Captured No Strength Signal

CCNC is the first corpus to explicitly distinguish causal, countercausal, and uncausal sentences, revealing that countercausal patterns are a major source of error in causality detection models (Cohen's κ = 0.74).

Converted directly from the paper's own repository (github.com/webis-de/arxiv-countercausality), not the Touché@CLEF26 shared task's TIRA-hosted variant of the same corpus.

Overview

Domain	News
Year	2025
Sentences	3,415
Granularity	Intra-sentence

Tasks & Splits

Task	Train	Test
Causality Detection	—	—
Causal Event Candidate Extraction	—	—
Causality Identification	—	—
Causality Extraction	—	—

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Citation

@article{hagen:2025a,
  title         = {Investigating {{Counterclaims}} in {{Causality Extraction}} from {{Text}}},
  author        = {Hagen, Tim and Deckers, Niklas and Wolter, Felix and Scells, Harrisen and Potthast, Martin},
  year          = 2025,
  journal       = {CoRR},
  volume        = {abs/2510.08224},
  eprint        = {2510.08224},
  doi           = {10.48550/ARXIV.2510.08224},
  urldate       = {2026-06-10},
  archiveprefix = {arXiv}
}