@inproceedings{priniski-etal-2023-pipeline,
title = "Pipeline for modeling causal beliefs from natural language",
author = "Priniski, John and
Verma, Ishaan and
Morstatter, Fred",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.41",
doi = "10.18653/v1/2023.acl-demo.41",
pages = "436--443",
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.",
}
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%0 Conference Proceedings
%T Pipeline for modeling causal beliefs from natural language
%A Priniski, John
%A Verma, Ishaan
%A Morstatter, Fred
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F priniski-etal-2023-pipeline
%X 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.
%R 10.18653/v1/2023.acl-demo.41
%U https://aclanthology.org/2023.acl-demo.41
%U https://doi.org/10.18653/v1/2023.acl-demo.41
%P 436-443
Markdown (Informal)
[Pipeline for modeling causal beliefs from natural language](https://aclanthology.org/2023.acl-demo.41) (Priniski et al., ACL 2023)
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.