Enhancing Named Entity Recognition for Agricultural Commodity Monitoring with Large Language Models-Reference-Cited by-同舟云学术

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Enhancing Named Entity Recognition for Agricultural Commodity Monitoring with Large Language Models

Published:2024-04-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th Workshop on Machine Learning and Systems
language:
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Author:

Chebbi Abir¹^ORCID,Kniesel Guido²^ORCID,Abdennadher Nabil³^ORCID,Dimarzo Giovanna⁴^ORCID

Affiliation:

1. university of applied sciences and arts western, Switzerland & University of Geneva, Geneva, Switzerland

2. Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland

3. university of applied sciences and arts western, Switzerland Geneva, Switzerland

4. University of Geneva, Geneva, Switzerland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3642970.3655846

Reference18 articles.

1. [n. d.]. Model Card and Evaluations for Claude Models Anthropic.

2. 2024. L'humain dans la boucle - Amazon SageMaker Ground Truth -AWS. https://aws.amazon.com/fr/sagemaker/groundtruth [Online; accessed 28. Mar. 2024].

3. William J Black Fabio Rinaldi and David Mowatt. [n. d.]. FACILE: DESCRIPTION OF THE NE SYSTEM USED FOR MUC-7.

4. A unified architecture for natural language processing

5. Sayan De Debarshi Kumar Sanyal and Imon Mukherjee. [n. d.]. AgriNER: An NER Dataset of Agricultural Entities for the Semantic Web. https://brat.nlplab.org

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1. Zero-shot evaluation of ChatGPT for food named-entity recognition and linking;Frontiers in Nutrition;2024-08-13

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