metadata
license: gpl-3.0
language:
- en
annotations_creators:
- expert-annotated
task_categories:
- text-classification
- token-classification
size_categories:
- 1k<n<10k
pretty_name: Causal Sentence and Cause–Effect Span Annotation
Causal Sentence and Cause–Effect Span Annotation
Dataset Summary
This dataset contains 3,014 annotated sentences for causal language analysis. Half of the sentences are labeled as causal, and half as non-causal. For causal sentences, annotations include cause and effect spans, along with explicit cause–effect links.
The annotations were created using the Doccano platform in a three-step process:
- Causality classification: Label each sentence as causal or non-causal.
- Span extraction: Mark the spans corresponding to cause(s) and effect(s).
- Linking: Connect each cause span to its corresponding effect span.
Annotation Process
A pilot study was conducted to assess inter-annotator reliability. Three authors independently annotated 60 sentences (20 per source). Agreement was measured using Krippendorff’s alpha (α) for each task.
- For span extraction, agreement was computed with partial matching (e.g., “poor sleeping during the night” vs. “poor sleeping” were considered a match).
- Average Krippendorff’s α across all tasks was 0.80, exceeding the standard reliability threshold (Krippendorff, 2004).
After validation, the lead author annotated the full dataset following the same guidelines.
Dataset Composition
- Total sentences: 3,014
- Non-causal sentences: ~50%
- Causal sentences: ~50% (with span and link annotations)
Data Splits
The dataset is divided as follows:
- Training: 70%
- Validation: 15%
- Test: 15%