--- license: other task_categories: - text-classification - token-classification language: - en multilinguality: - monolingual size_categories: - n<1K tags: - causality pretty_name: TCR configs: - config_name: causality detection data_files: - split: train path: causality-detection/train.parquet - split: validation path: causality-detection/dev.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: validation path: causal-candidate-extraction/dev.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: validation path: causality-identification/dev.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 TCR corpus into hf datasets. Please find the original dataset > [here](https://cogcomp.seas.upenn.edu/page/resource_view/118). The data is sourced from the > [CREST](https://github.com/phosseini/CREST) aggregation. Please see the [citations](#citations) at the end of this README. ## Dataset Description - **Homepage:** https://cogcomp.seas.upenn.edu/page/resource_view/118 - **Paper:** [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://aclanthology.org/P18-1212/) # Usage ## Causality Detection ```py from datasets import load_dataset dataset = load_dataset("thagen/TCR", "causality detection") ``` ## Causal Candidate Extraction ```py from datasets import load_dataset dataset = load_dataset("thagen/TCR", "causal candidate extraction") ``` ## Causality Identification ```py from datasets import load_dataset dataset = load_dataset("thagen/TCR", "causality identification") ``` # Citations The TCR paper by [Ning et al., 2018](https://aclanthology.org/P18-1212/): ```bib @inproceedings{ning:2018, title = {Joint {{Reasoning}} for {{Temporal}} and {{Causal Relations}}}, booktitle = {Proceedings of the 56th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}, {{ACL}} 2018, {{Melbourne}}, {{Australia}}, {{July}} 15-20, 2018, {{Volume}} 1: {{Long Papers}}}, author = {Ning, Qiang and Feng, Zhili and Wu, Hao and Roth, Dan}, editor = {Gurevych, Iryna and Miyao, Yusuke}, year = {2018}, pages = {2278--2288}, publisher = {Association for Computational Linguistics}, doi = {10.18653/V1/P18-1212}, urldate = {2026-06-10} } ``` CREST by [Hosseini et al., 2021](https://arxiv.org/abs/2103.13606) — whose aggregation we used to source the TCR data: ```bib @article{hosseini:2021, title = {Predicting {{Directionality}} in {{Causal Relations}} in {{Text}}}, author = {Hosseini, Pedram and Broniatowski, David A. and Diab, Mona T.}, year = {2021}, journal = {CoRR}, volume = {abs/2103.13606}, eprint = {2103.13606}, archiveprefix = {arXiv} } ```