README.md

---
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  # Does not really make sense but exists to have the same labels as the classification task
            '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 the BECausE corpus into hf datasets. It is in conformance with BECausE's MIT license. Please find the original dataset
> [here](https://github.com/duncanka/BECAUSE). We used the [UniCausal](https://github.com/tanfiona/UniCausal/tree/main/data/splits) reformatting of the data as the basis
> for this repository. Please see the [citations](#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](https://doi.org/10.18653/v1/W17-0812)

# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("webis/BECauSEv2", "causality detection")
```

## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("webis/BECauSEv2", "causal candidate extraction")
```

## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("webis/BECauSv2", "causality identification")
```

# Citations

The BECauSE v2.0 paper by [Dunietz et al., 2017](https://www.cs.cmu.edu/~jdunietz/publications/because-v2.pdf):
```bib
@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](https://link.springer.com/chapter/10.1007/978-3-031-39831-5_23) &mdash; who's dataformat we used to make BECausE 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}
}
```