yuzhimanhua/Multi-BioNER
Cross-type Biomedical Named Entity Recognition with Deep Multi-task Learning (Bioinformatics'19)
This project helps biomedical researchers, pharmacologists, and biologists automatically identify key biological entities in scientific text. It takes raw biomedical text, such as research abstracts or articles, and highlights specific terms like genes, proteins, chemicals, and diseases within the content. The output is annotated text, making it easier to extract crucial information and insights from large volumes of scientific literature.
135 stars. No commits in the last 6 months.
Use this if you need to accurately and efficiently identify and categorize biological entities like genes, chemicals, and diseases within large collections of biomedical text.
Not ideal if your primary need is general-purpose named entity recognition outside of the biomedical domain, or if you require real-time, low-latency annotation for very short text snippets.
Stars
135
Forks
27
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 25, 2024
Commits (30d)
0
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