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license: cc-by-nc-4.0
task_categories:
- text-classification
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
tags:
- causality
pretty_name: PolitiCause
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: causal candidate extraction
data_files:
- split: train
path: causal-candidate-extraction/train.parquet
- split: test
path: causal-candidate-extraction/test.parquet
features:
- name: index
dtype: string
- name: text
dtype: string
- name: entity
sequence:
sequence: int32
- 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: causal candidate extraction
task: token-classification
task_id: token_classification
splits:
train_split: train
eval_split: test
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 PolitiCause into hf datasets. Please find the original dataset
> [here](https://github.com/pgarco/PolitiCAUSE). The original release only ships sentence-level
> causal/noncausal labels (`train.csv`/`val.csv`/`test.csv`, used for `causality detection` here) plus a
> SEPARATE multi-annotator span file (`span_annotations.csv`, 2-9 independent passes per sentence) that the
> source paper itself never reduces to one gold span set (it reports no extraction results). The `causal
> candidate extraction`/`causality identification` configs here are therefore DERIVED, not verbatim: for each
> gold-causal sentence, one canonical cause/effect pair is picked via a documented reconciliation policy
> (prefer a balanced, non-empty Cause+Effect annotator pass, tie-broken by that annotator's own confidence;
> drop the sentence if no annotator pass qualifies at all). This drops ~9% of gold-causal sentences and loses
> an extra span on another ~9% of the kept ones — see `conversion_script.py`'s module docstring for the full
> policy and exact counts before relying on these two configs.
## Dataset Description
- **Repository:** https://github.com/pgarco/PolitiCAUSE
- **Paper:** [PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts](https://aclanthology.org/2024.lrec-main.1124/)
# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/PolitiCause", "causality detection")
```
## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("thagen/PolitiCause", "causal candidate extraction")
```
## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/PolitiCause", "causality identification")
```
# Citations
```bib
@inproceedings{corral:2024,
title = {{{PolitiCause}}: {{An Annotation Scheme}} and {{Corpus}} for {{Causality}} in {{Political Texts}}},
shorttitle = {{{PolitiCause}}},
booktitle = {Proceedings of the 2024 {{Joint International Conference}} on {{Computational Linguistics}}, {{Language Resources}} and {{Evaluation}}, {{LREC}}/{{COLING}} 2024, 20-25 {{May}}, 2024, {{Torino}}, {{Italy}}},
author = {Corral, Paulina Garcia and B{\'e}chara, Hanna and Zhang, Ran and Jankin, Slava},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, V{\'e}ronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
year = 2024,
pages = {12836--12845},
publisher = {{ELRA and ICCL}},
url = {https://aclanthology.org/2024.lrec-main.1124},
urldate = {2025-02-08}
}
``` |