metadata
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
This repository integrates the AltLex corpus into hf datasets. 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/chridey/altlex
- Paper: Identifying Causal Relations Using Parallel Wikipedia Articles
Usage
Causality Detection
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causality detection")
Causal Candidate Extraction
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causal candidate extraction")
Causality Identification
from datasets import load_dataset
dataset = load_dataset("thagen/AltLex", "causality identification")
Citations
The AltLex paper by Hidey and McKeown, 2016:
@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 — who's dataformat we used to make AltLex 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}
}