TCR

TCR (Temporal and Causal Reasoning) provides joint temporal and causal relation annotations over a small but densely annotated set of news articles, enabling reasoning that combines both relation types.

Overview

Domain	News
Year	2018
Sentences	172

Tasks & Splits

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

Data Explorer

Causality Detection

Causal Event Candidate Extraction

Causality Identification

Citation

@inproceedings{ning:2018,
  title     = {Joint {{Reasoning}} for {{Temporal}} and {{Causal Relations}}},
  booktitle = {Proceedings of the 56th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}, {{ACL}} 2018, {{Melbourne}}, {{Australia}}, {{July}} 15-20, 2018, {{Volume}} 1: {{Long Papers}}},
  author    = {Ning, Qiang and Feng, Zhili and Wu, Hao and Roth, Dan},
  editor    = {Gurevych, Iryna and Miyao, Yusuke},
  year      = 2018,
  pages     = {2278--2288},
  publisher = {Association for Computational Linguistics},
  doi       = {10.18653/V1/P18-1212},
  urldate   = {2026-06-10}
}