Summary of On Fusing Chatgpt and Ensemble Learning in Discon-tinuous Named Entity Recognition in Health Corpora, by Tzu-chieh Chen and Wen-yang Lin
On Fusing ChatGPT and Ensemble Learning in Discon-tinuous Named Entity Recognition in Health Corpora
by Tzu-Chieh Chen, Wen-Yang Lin
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach to Discontinuous Named Entity Recognition (DNER) by integrating large language models like ChatGPT within an ensemble method. The study combines five state-of-the-art NER models with ChatGPT using custom prompt engineering, demonstrating its potential to enhance performance on DNER tasks in the healthcare domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how ChatGPT can be used as a problem-solving tool and an integrative element within ensemble learning algorithms for Discontinuous Named Entity Recognition. By combining five state-of-the-art NER models with ChatGPT, the study shows that its proposed method outperforms existing approaches in three benchmark medical datasets. |
Keywords
» Artificial intelligence » Named entity recognition » Ner » Prompt
