Summary of Joint Extraction Of Uyghur Medicine Knowledge with Edge Computing, by Fan Lu et al.
Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
by Fan Lu, Quan Qi, Huaibin Qin
First submitted to arxiv on: 13 Jan 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 This paper proposes a novel approach to medical knowledge extraction on edge devices using deep learning models. The goal is to achieve localized entity and relation extraction while safeguarding sensitive healthcare data by avoiding cloud-based processing. Existing methods rely on sequential pipelines, which can lead to error propagation and neglect interrelations between entities and relations. To address these challenges, the authors propose CoEx-Bert, a joint extraction model with shared parameterization between two models for entity and relation recognition. This approach leverages contextual relations to resolve overlapping entity issues in unstructured Uyghur medical texts. Experimental results demonstrate significant improvements over existing state-of-the-art methods on the Uyghur traditional medical literature dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors and researchers find important information in medical books and documents. Right now, this process happens in big data centers that can be far away from where the information was written. This can be a problem because it means sensitive medical information has to travel long distances. To solve this issue, scientists created a new way to use computers called CoEx-Bert. It allows them to look at text and find important words and connections between them without sending all the information to faraway places. This helps keep private medical information safe while still letting doctors and researchers find what they need. |
Keywords
* Artificial intelligence * Bert * Deep learning
