README.md

metadata

license: mit
task_categories:
  - text-classification
  - token-classification
language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
tags:
  - causality
pretty_name: BECausE v2
paperswithcode_id: ../paper/the-because-corpus-20-annotating-causality
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 BECausE corpus into hf datasets. It is in conformance with BECausE's MIT license. Please find the original dataset here. We used the UniCausal reformatting of the data as the basis for this repository. Please see the citations at the end of this README.

Dataset Description

• Repository: https://github.com/duncanka/BECAUSE
• Paper: The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations

Usage

Causality Detection

from datasets import load_dataset
dataset = load_dataset("webis/BECauSEv2", "causality detection")

Causal Candidate Extraction

from datasets import load_dataset
dataset = load_dataset("webis/BECauSEv2", "causal candidate extraction")

Causality Identification

from datasets import load_dataset
dataset = load_dataset("webis/BECauSv2", "causality identification")

Citations

The BECauSE v2.0 paper by Dunietz et al., 2017:

@inproceedings{dunietz:2017,
  title = {The {{BECauSE Corpus}} 2.0: {{Annotating Causality}} and {{Overlapping Relations}}},
  shorttitle = {The {{BECauSE Corpus}} 2.0},
  booktitle = {Proceedings of the 11th {{Linguistic Annotation Workshop}}, {{LAW}}@{{EACL}} 2017, {{Valencia}}, {{Spain}}, {{April}} 3, 2017},
  author = {Dunietz, Jesse and Levin, Lori S. and Carbonell, Jaime G.},
  editor = {Schneider, Nathan and Xue, Nianwen},
  year = {2017},
  pages = {95--104},
  publisher = {Association for Computational Linguistics},
  doi = {10.18653/V1/W17-0812}
}

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