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# license: cc-by-4.0 # TODO: verify — https://github.com/cltl/EventStoryLine
task_categories:
- text-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
tags:
- causality
pretty_name: EventStoryLine (ESL)
configs:
- config_name: causality detection
data_files:
- split: train
path: causality-detection/train.parquet
- split: test
path: causality-detection/test.parquet
features:
- name: index
dtype: string
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': uncausal
'1': causal
- config_name: causality identification
data_files:
- split: train
path: causality-identification/train.parquet
- split: test
path: causality-identification/test.parquet
features:
- name: index
dtype: string
- name: text
dtype: string
- name: relations
list:
- name: relationship
dtype:
class_label:
names:
'0': no-rel
'1': causal
- name: first
dtype: string
- name: second
dtype: string
train-eval-index:
- config: causality detection
task: text-classification
task_id: text_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: label
metrics:
- type: accuracy
- type: precision
- type: recall
- type: f1
- config: causality identification
task: text-classification
task_id: text_classification
splits:
train_split: train
eval_split: test
metrics:
- type: accuracy
- type: precision
- type: recall
- type: f1
---
> [!NOTE]
> This repository integrates the EventStoryLine (ESL) corpus into hf datasets. Please find the original dataset
> [here](https://github.com/cltl/EventStoryLine). We used the [UniCausal](https://github.com/tanfiona/UniCausal/tree/main/data/splits) reformatting of the data (referred to as `esl2`) as the basis
> for this repository. Please see the [citations](#citations) at the end of this README.
## Dataset Description
- **Repository:** https://github.com/cltl/EventStoryLine
- **Paper:** [The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction](https://aclanthology.org/W17-2711)
# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/EventStoryLine", "causality detection")
```
## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/EventStoryLine", "causality identification")
```
# Citations
The EventStoryLine paper by [Caselli and Vossen, 2017](https://aclanthology.org/W17-2711):
```bib
@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](https://link.springer.com/chapter/10.1007/978-3-031-39831-5_23) — who's dataformat we used to make ESL compatible with hf datasets:
```bib
@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}
}
```
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