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--- |
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language: |
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- en |
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tags: |
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- legal |
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- finance |
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- climate |
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- social |
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pretty_name: 'CoCoHD: Congress Committee Hearing Dataset - Transcripts' |
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size_categories: |
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- 10K<n<100K |
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license: cc |
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--- |
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## Dataset Summary |
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The Congress Committee Hearing Dataset (CoCoHD) Transcripts comprises transcripts from congressional (House and Senate) hearings from 1997 to 2024. |
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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. |
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## Dataset Structure |
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The hearing transcripts of each Congress session are kept in a folder and in text format. |
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### Related Datasets |
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- [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. |
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- [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. |
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These datasets offer valuable metadata that complements the CoCoHD transcripts, enabling more detailed and accurate analyses of congressional hearings. |
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## Licensing |
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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. |
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## Citation |
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If you utilize this dataset in your research, please cite it as follows: |
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``` |
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@inproceedings{hiray-etal-2024-cocohd, |
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title = "{C}o{C}o{HD}: Congress Committee Hearing Dataset", |
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author = "Hiray, Arnav and |
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Liu, Yunsong and |
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Song, Mingxiao and |
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Shah, Agam and |
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Chava, Sudheer", |
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editor = "Al-Onaizan, Yaser and |
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Bansal, Mohit and |
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Chen, Yun-Nung", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", |
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month = nov, |
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year = "2024", |
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address = "Miami, Florida, USA", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.findings-emnlp.911", |
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doi = "10.18653/v1/2024.findings-emnlp.911", |
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pages = "15529--15542", |
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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.", |
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} |
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``` |
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## GitHub Link |
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- [Link to our GitHub repository.](https://github.com/gtfintechlab/CoCoHD) |
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## Contact Information |
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Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions. |
