Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction

Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, Thanh Lam Hoang


Abstract
The Zero-Shot Learning (ZSL) task pertains to the identification of entities or relations in texts that were not seen during training. ZSL has emerged as a critical research area due to the scarcity of labeled data in specific domains, and its applications have grown significantly in recent years. With the advent of large pretrained language models, several novel methods have been proposed, resulting in substantial improvements in ZSL performance. There is a growing demand, both in the research community and industry, for a comprehensive ZSL framework that facilitates the development and accessibility of the latest methods and pretrained models. In this study, we propose a novel ZSL framework called Zshot that aims to address the aforementioned challenges. Our primary objective is to provide a platform that allows researchers to compare different state-of-the-art ZSL methods with standard benchmark datasets. Additionally, we have designed our framework to support the industry with readily available APIs for production under the standard SpaCy NLP pipeline. Our API is extendible and evaluable, moreover, we include numerous enhancements such as boosting the accuracy with pipeline ensembling and visualization utilities available as a SpaCy extension.
Anthology ID:
2023.acl-demo.34
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Danushka Bollegala, Ruihong Huang, Alan Ritter
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
357–368
Language:
URL:
https://aclanthology.org/2023.acl-demo.34
DOI:
10.18653/v1/2023.acl-demo.34
Bibkey:
Cite (ACL):
Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, and Thanh Lam Hoang. 2023. Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 357–368, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (Picco et al., ACL 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2023.acl-demo.34.pdf
Video:
 https://preview.aclanthology.org/naacl24-info/2023.acl-demo.34.mp4