Optimizing context-based location extraction by tuning open-source LLMs with RAG

Optimizing context-based location extraction by tuning open-source LLMs with RAG

Text data such as news from media include different types of geographic information, represented by location, that indicates the whereabout of events or phenomena. Extracting the geographic locations from text within their contexts is challenging, even with Natural Language Processing (NLP) tools an...

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Bibliographic Details
Main Authors: Zifu Wang, Yahya Masri, Anusha Srirenganathan Malarvizhi, Tayven Stover, Samir Ahmed, David Wong, Yongyao Jiang, Yun Li, Mathieu Bere, Daniel Rothbart, Dieter Pfoser, David Marshall, Chaowei Yang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
Subjects:
Context-based location extraction
large language model
retrieval augmented generation
natural language processing
Sudan conflict
media
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2521786
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https://www.tandfonline.com/doi/10.1080/17538947.2025.2521786

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