| --- |
| language: |
| - en |
| tags: |
| - legal |
| - finance |
| - climate |
| - social |
| pretty_name: 'CoCoHD: Congress Committee Hearing Dataset - Transcripts' |
| size_categories: |
| - 10K<n<100K |
| license: cc |
| --- |
| |
| ## Dataset Summary |
|
|
| The Congress Committee Hearing Dataset (CoCoHD) Transcripts comprises transcripts from congressional (House and Senate) hearings from 1997 to 2024. |
|
|
| This dataset is designed to facilitate research in natural language processing (NLP). It contains 30k+ U.S. congressional hearing transcripts from the 105th Congress to the 118th. |
|
|
| ## Dataset Structure |
|
|
| The hearing transcripts of each Congress session are kept in a folder and in text format. |
|
|
| ### Related Datasets |
|
|
| - [CoCoHD Hearing Details](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details): This dataset provides comprehensive metadata for each congressional hearing, including information such as the hearing title, date, committee, and witnesses. |
|
|
| - [CoCoHD Hearing Details Cleaned](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details_cleaned): A refined version of the hearing details dataset, this collection has been processed to correct inconsistencies, standardize committee names, and remove duplicate or erroneous records, ensuring higher data quality for analysis. |
|
|
| These datasets offer valuable metadata that complements the CoCoHD transcripts, enabling more detailed and accurate analyses of congressional hearings. |
|
|
| ## Licensing |
|
|
| The CoCoHD Transcripts dataset is released under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/). This permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
|
|
| ## Citation |
|
|
| If you utilize this dataset in your research, please cite it as follows: |
|
|
| ``` |
| @inproceedings{hiray-etal-2024-cocohd, |
| title = "{C}o{C}o{HD}: Congress Committee Hearing Dataset", |
| author = "Hiray, Arnav and |
| Liu, Yunsong and |
| Song, Mingxiao and |
| Shah, Agam and |
| Chava, Sudheer", |
| editor = "Al-Onaizan, Yaser and |
| Bansal, Mohit and |
| Chen, Yun-Nung", |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", |
| month = nov, |
| year = "2024", |
| address = "Miami, Florida, USA", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2024.findings-emnlp.911", |
| doi = "10.18653/v1/2024.findings-emnlp.911", |
| pages = "15529--15542", |
| 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.", |
| } |
| ``` |
|
|
| ## GitHub Link |
| - [Link to our GitHub repository.](https://github.com/gtfintechlab/CoCoHD) |
|
|
|
|
| ## Contact Information |
|
|
| Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions. |
