README.md

metadata

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

This repository integrates PolitiCause into hf datasets. Please find the original dataset here. 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

Usage

Causality Detection

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

Causal Candidate Extraction

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

Causality Identification

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

Citations

@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}
}