thagen/CausalNewsCorpus

This repository integrates the Causal News Corpus (CNC) into hf datasets. Please find the original dataset here. We used the UniCausal reformatting of the data as the basis for this repository. Please see the citations at the end of this README.

Usage

Causality Detection

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpus", "causality detection")

Causal Candidate Extraction

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpus", "causal candidate extraction")

Causality Identification

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpus", "causality identification")

Citations

The Causal News Corpus paper by Tan et al., 2022:

@inproceedings{tan:2022,
  title = {The Causal News Corpus: Annotating Causal Relations in Event Sentences},
  booktitle = {Proceedings of the 13th Language Resources and Evaluation Conference},
  author = {Tan, Fiona Anting and Ng, See-Kiong and Ong, Alifia Reina},
  year = {2022},
  pages = {2298--2310},
  publisher = {European Language Resources Association}
}

UniCausal by Tan et al., 2023 — who's dataformat we used to make CNC compatible with hf datasets:

@inproceedings{tan:2023,
  title = {{{UniCausal}}: {{Unified Benchmark}} and {{Repository}} for {{Causal Text Mining}}},
  shorttitle = {{{UniCausal}}},
  booktitle = {Big {{Data Analytics}} and {{Knowledge Discovery}} - 25th {{International Conference}}, {{DaWaK}} 2023, {{Penang}}, {{Malaysia}}, {{August}} 28-30, 2023, {{Proceedings}}},
  author = {Tan, Fiona Anting and Zuo, Xinyu and Ng, See-Kiong},
  editor = {Wrembel, Robert and Gamper, Johann and Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail},
  year = {2023},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14148},
  pages = {248--262},
  publisher = {Springer},
  doi = {10.1007/978-3-031-39831-5_23}
}