---
# license: unknown
task_categories:
- text-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
tags:
- causality
pretty_name: SemEval 2010 Task 8
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: 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: 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 causal subset of SemEval 2010 Task 8 into hf datasets. Please find the original dataset
> [here](https://semeval.github.io/SemEval2010/). We used the [UniCausal](https://github.com/tanfiona/UniCausal/tree/main/data/splits) reformatting of the data as the basis
> for this repository. Note that only the Cause-Effect relation pairs are retained; other relation types are discarded. Please see the [citations](#citations) at the end of this README.

## Dataset Description

- **Homepage:** https://semeval.github.io/SemEval2010/
- **Paper:** [SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals](https://aclanthology.org/S10-1006)

# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/SemEval2010T8", "causality detection")
```

## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/SemEval2010T8", "causality identification")
```

# Citations

The SemEval 2010 Task 8 paper by [Hendrickx et al., 2010](https://aclanthology.org/S10-1006):
```bib
@inproceedings{hendrickx:2010,
  title = {{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals},
  booktitle = {Proceedings of the 5th International Workshop on Semantic Evaluation},
  author = {Hendrickx, Iris and Kim, Su Nam and Kozareva, Zornitsa and Nakov, Preslav and {\'O} S{\'e}aghdha, Diarmuid and Pad{\'o}, Sebastian and Pennacchiotti, Marco and Romano, Lorenza and Szpakowicz, Stan},
  year = {2010},
  pages = {33--38},
  publisher = {Association for Computational Linguistics}
}
```

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 SemEval 2010 Task 8 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}
}
```