Published June 30, 2023
| Version v1
Poster
Open
Large Language Models and NER: better results with less work
Authors/Creators
- 1. Cornell University, United States of America
Contributors
Data manager (3):
Hosting institution:
- 1. University of Graz
- 2. Belgrade Center for Digital Humanities
- 3. Le Mans Université
- 4. Digital Humanities im deutschsprachigen Raum
Description
Our work considers how new advances in pretrained text-to-text generation models might make named entity recognition more accurate, flexible, and streamlined for the digital humanist. We provide an example of how text-to-text generative models can identify mentions of characters, authors, and book names within Goodreads book reviews.
Files
Thalken_Large-Language-Models-and-NER.pdf
Files
(1.7 MB)
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Additional details
Related works
- Is supplement to
- Conference paper: 10.5281/zenodo.8107551 (DOI)