README.md

---
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
---

> [!NOTE]
> This repository integrates the "V2" release of the Causal News Corpus (CNC) — published as RECESS — into hf
> datasets. Please find the original dataset [here](https://github.com/tanfiona/CausalNewsCorpus). 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](../CNC), the original 2022 release (183
> causal relations) — kept as its own separate dataset for comparison rather than silently overwritten.
> Please see the [citations](#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](https://aclanthology.org/2023.ijcnlp-main.6/)

# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causality detection")
```

## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causal candidate extraction")
```

## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/CausalNewsCorpusV2", "causality identification")
```

# Citations

This "V2" release is published as RECESS, [Tan et al., 2023](https://aclanthology.org/2023.ijcnlp-main.6/):
```bib
@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](https://aclanthology.org/2022.lrec-1.246):
```bib
@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}
}
```