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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:
1. **Causality classification**: Label each sentence as causal or non-causal.
2. **Span extraction**: Mark the spans corresponding to cause(s) and effect(s).
3. **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%
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