thagen/EventStoryLine

This repository integrates the EventStoryLine (ESL) corpus into hf datasets. Please find the original dataset here. We used the UniCausal reformatting of the data (referred to as esl2) 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/EventStoryLine", "causality detection")

Causality Identification

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

Citations

The EventStoryLine paper by Caselli and Vossen, 2017:

@inproceedings{caselli:2017,
  title = {The Event {StoryLine} Corpus: {A} New Benchmark for Causal and Temporal Relation Extraction},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  author = {Caselli, Tommaso and Vossen, Piek},
  year = {2017},
  pages = {77--86},
  publisher = {Association for Computational Linguistics},
  doi = {10.18653/v1/W17-2711}
}

UniCausal by Tan et al., 2023 — who's dataformat we used to make ESL 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}
}