Summary of Generalized Knowledge-enhanced Framework For Biomedical Entity and Relation Extraction, by Minh Nguyen and Phuong Le
Generalized knowledge-enhanced framework for biomedical entity and relation extraction
by Minh Nguyen, Phuong Le
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 paper introduces a novel framework for biomedical entity and relation extraction that leverages external knowledge to construct a reusable background knowledge graph. Inspired by human learning processes, the framework utilizes common-knowledge-sharing mechanisms to build a general neural-network knowledge graph that can be transferred to different domain-specific biomedical texts effectively. The model is evaluated on benchmark datasets such as BioRelEx for binding interaction detection and ADE for Adverse Drug Effect identification, achieving competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand biological research papers by developing a new way to extract important information from these texts. It’s like how we learn new things by starting with the basics and then using that knowledge to learn more about specialized topics. The researchers created a special kind of computer model that can learn this basic knowledge and apply it to different areas of biology, making it really good at finding specific types of information in papers. |
Keywords
» Artificial intelligence » Knowledge graph » Neural network
