Abstract
We explore computational methods for perceived agency attribution in natural language data. We consider ‘agency’ as the freedom and capacity to act, and the corresponding Natural Language Processing (NLP) task involves automatically detecting attributions of agency to entities in text. Our theoretical framework draws on semantic frame analysis, role labelling and related techniques. In initial experiments, we focus on the perceived agency of AI systems. To achieve this, we analyse a dataset of English-language news coverage of AI-related topics, published within one year surrounding the release of the Large Language Model-based service ChatGPT, a milestone in the general public’s awareness of AI. Building on this, we propose a schema to annotate a dataset for agency attribution and formulate additional research questions to answer by applying NLP models.- Anthology ID:
- 2024.eacl-srw.15
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Neele Falk, Sara Papi, Mike Zhang
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 208–214
- Language:
- URL:
- https://aclanthology.org/2024.eacl-srw.15
- DOI:
- Cite (ACL):
- Igor Ryazanov and Johanna Björklund. 2024. Thesis Proposal: Detecting Agency Attribution. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 208–214, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Thesis Proposal: Detecting Agency Attribution (Ryazanov & Björklund, EACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.eacl-srw.15.pdf