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Scientific Research, LLM
An Empirical Study on Extracting Named Entities from Repetitive Texts
An overview of the paper I presented at the WEBIST 2024 conference.
Large Language Models (LLMs) like GPT-3.5 Turbo and GPT-4 are transforming how we process and analyze text. Their capabilities expand into various fields, including healthcare, finance, and historical research, from summarizing data to extracting structured information.
In this post, I explore a study that tested the effectiveness of LLMs in extracting named entities from repetitive texts — specifically, historical birth registries. I’ll dive into the results, challenges, ethical considerations, and broader applications.
Whether you’re a researcher, developer, or business professional, this post offers practical takeaways on leveraging LLMs for real-world tasks.
The Challenge of Named Entity Recognition (NER)
Named Entity Recognition (NER) involves identifying and categorizing specific information (e.g., names, dates, and locations) from text. While modern methods excel with structured or annotated datasets, repetitive texts present unique challenges due to inconsistent formatting and context-specific terms.
