| --- |
| |
| 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) — 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} |
| } |
| ``` |
|
|