AltLex / README.md
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---
# license: mit # TODO: verify — https://github.com/chridey/altlex
task_categories:
- text-classification
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
tags:
- causality
pretty_name: AltLex
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 AltLex corpus into hf datasets. Please find the original dataset
> [here](https://github.com/chridey/altlex). We used the [UniCausal](https://github.com/tanfiona/UniCausal/tree/main/data/grouped/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/chridey/altlex
- **Paper:** [Identifying Causal Relations Using Parallel Wikipedia Articles](https://doi.org/10.18653/v1/P16-1135)
# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causality detection")
```
## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causal candidate extraction")
```
## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causality identification")
```
# Citations
The AltLex paper by [Hidey and McKeown, 2016](https://doi.org/10.18653/v1/P16-1135):
```bib
@inproceedings{hidey:2016,
title = {Identifying Causal Relations Using Parallel {Wikipedia} Articles},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
author = {Hidey, Christopher and McKeown, Kathleen},
year = {2016},
pages = {1424--1433},
publisher = {Association for Computational Linguistics},
doi = {10.18653/v1/P16-1135}
}
```
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 AltLex 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}
}
```