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    <abstract>U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee’s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.</abstract>
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%0 Conference Proceedings
%T CoCoHD: Congress Committee Hearing Dataset
%A Hiray, Arnav
%A Liu, Yunsong
%A Song, Mingxiao
%A Shah, Agam
%A Chava, Sudheer
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hiray-etal-2024-cocohd
%X U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee’s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.
%U https://aclanthology.org/2024.findings-emnlp.911
%P 15529-15542

Markdown (Informal)

[CoCoHD: Congress Committee Hearing Dataset](https://aclanthology.org/2024.findings-emnlp.911) (Hiray et al., Findings 2024)

• CoCoHD: Congress Committee Hearing Dataset (Hiray et al., Findings 2024)

ACL

• Arnav Hiray, Yunsong Liu, Mingxiao Song, Agam Shah, and Sudheer Chava. 2024. CoCoHD: Congress Committee Hearing Dataset. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15529–15542, Miami, Florida, USA. Association for Computational Linguistics.