Datasets:

thagen
/

EventStoryLine

task_categories:
  - text-classification
language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
tags:
  - causality
pretty_name: EventStoryLine (ESL)
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

This repository integrates the EventStoryLine (ESL) corpus into hf datasets. Please find the original dataset here. We used the UniCausal reformatting of the data (referred to as esl2) as the basis for this repository. Please see the citations at the end of this README.

Dataset Description

Repository: https://github.com/cltl/EventStoryLine
Paper: The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction

Usage

Causality Detection

from datasets import load_dataset
dataset = load_dataset("thagen/EventStoryLine", "causality detection")

Causality Identification

from datasets import load_dataset
dataset = load_dataset("thagen/EventStoryLine", "causality identification")

Citations

The EventStoryLine paper by Caselli and Vossen, 2017:

@inproceedings{caselli:2017,
  title = {The Event {StoryLine} Corpus: {A} New Benchmark for Causal and Temporal Relation Extraction},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  author = {Caselli, Tommaso and Vossen, Piek},
  year = {2017},
  pages = {77--86},
  publisher = {Association for Computational Linguistics},
  doi = {10.18653/v1/W17-2711}
}

UniCausal by Tan et al., 2023 — who's dataformat we used to make ESL 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}
}