Pipeline for modeling causal beliefs from natural language
Abstract
We present a causal language analysis pipeline that leverages a Large Language Model to identify causal claims made in natural language documents, and aggregates claims across a corpus to produce a causal claim network. The pipeline then applies a clustering algorithm that groups causal claims based on their semantic topics. We demonstrate the pipeline by modeling causal belief systems surrounding the Covid-19 vaccine from tweets.- Anthology ID:
- 2023.acl-demo.41
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Danushka Bollegala, Ruihong Huang, Alan Ritter
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 436–443
- Language:
- URL:
- https://aclanthology.org/2023.acl-demo.41
- DOI:
- 10.18653/v1/2023.acl-demo.41
- Cite (ACL):
- John Priniski, Ishaan Verma, and Fred Morstatter. 2023. Pipeline for modeling causal beliefs from natural language. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 436–443, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Pipeline for modeling causal beliefs from natural language (Priniski et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2023.acl-demo.41.pdf