@inproceedings{antoniak-etal-2023-riveter,
title = "Riveter: Measuring Power and Social Dynamics Between Entities",
author = "Antoniak, Maria and
Field, Anjalie and
Mun, Jimin and
Walsh, Melanie and
Klein, Lauren and
Sap, Maarten",
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.36",
doi = "10.18653/v1/2023.acl-demo.36",
pages = "377--388",
abstract = "Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.",
}
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<abstract>Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.</abstract>
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%0 Conference Proceedings
%T Riveter: Measuring Power and Social Dynamics Between Entities
%A Antoniak, Maria
%A Field, Anjalie
%A Mun, Jimin
%A Walsh, Melanie
%A Klein, Lauren
%A Sap, Maarten
%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 antoniak-etal-2023-riveter
%X Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.
%R 10.18653/v1/2023.acl-demo.36
%U https://aclanthology.org/2023.acl-demo.36
%U https://doi.org/10.18653/v1/2023.acl-demo.36
%P 377-388
Markdown (Informal)
[Riveter: Measuring Power and Social Dynamics Between Entities](https://aclanthology.org/2023.acl-demo.36) (Antoniak et al., ACL 2023)
ACL
- Maria Antoniak, Anjalie Field, Jimin Mun, Melanie Walsh, Lauren Klein, and Maarten Sap. 2023. Riveter: Measuring Power and Social Dynamics Between Entities. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 377–388, Toronto, Canada. Association for Computational Linguistics.