README.md

metadata

license: cc0-1.0
task_categories:
  - text-classification
  - token-classification
language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
tags:
  - causality
pretty_name: Causal News Corpus V2 / RECESS (CNCv2)
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 the "V2" release of the Causal News Corpus (CNC) — published as RECESS — into hf datasets. Please find the original dataset here. This is the actively-maintained release the maintainers recommend using ("For 2023 Shared Task, please use V2"), with far richer span annotations (2257 causal relations) than CNC, the original 2022 release (183 causal relations) — kept as its own separate dataset for comparison rather than silently overwritten. Please see the citations at the end of this README.

Dataset Description

• Repository: https://github.com/tanfiona/CausalNewsCorpus
• Paper: RECESS: Resource for Extracting Cause, Effect, and Signal Spans

Usage

Causality Detection

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causality detection")

Causal Candidate Extraction

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causal candidate extraction")

Causality Identification

from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causality identification")

Citations

This "V2" release is published as RECESS, Tan et al., 2023:

@inproceedings{tan-etal-2023-recess,
  title = {{RECESS}: Resource for Extracting Cause, Effect, and Signal Spans},
  booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  author = {Tan, Fiona Anting and Hettiarachchi, Hansi and H{\"u}rriyeto{\u{g}}lu, Ali and Oostdijk, Nelleke and Caselli, Tommaso and Nomoto, Tadashi and Uca, Onur and Liza, Farhana Ferdousi and Ng, See-Kiong},
  year = {2023},
  address = {Nusa Dua, Bali},
  publisher = {Association for Computational Linguistics},
  pages = {66--82}
}

The original 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}
}