Abstract
Extracting statements about causality from text documents is a challenging task in the absence of annotated training data. We create a search system for causal statements about user-specified concepts by combining pattern matching of causal connectives with semantic similarity ranking, using a language model fine-tuned for semantic textual similarity. Preliminary experiments on a small test set from Swedish governmental reports show promising results in comparison to two simple baselines.- Anthology ID:
- 2023.resourceful-1.4
- Volume:
- Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)
- Month:
- May
- Year:
- 2023
- Address:
- Tórshavn, the Faroe Islands
- Editors:
- Nikolai Ilinykh, Felix Morger, Dana Dannélls, Simon Dobnik, Beáta Megyesi, Joakim Nivre
- Venue:
- RESOURCEFUL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–29
- Language:
- URL:
- https://aclanthology.org/2023.resourceful-1.4
- DOI:
- Cite (ACL):
- Luise Dürlich, Joakim Nivre, and Sara Stymne. 2023. What Causes Unemployment? Unsupervised Causality Mining from Swedish Governmental Reports. In Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023), pages 25–29, Tórshavn, the Faroe Islands. Association for Computational Linguistics.
- Cite (Informal):
- What Causes Unemployment? Unsupervised Causality Mining from Swedish Governmental Reports (Dürlich et al., RESOURCEFUL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.resourceful-1.4.pdf