izuna385/Entity-Linking-Tutorial

Bi-encoder Based Entity Linking Tutorial. You can run experiment only in 5 minutes. Experiments on Co-lab pro GPU are also supported!

23
/ 100
Experimental

This project helps researchers and practitioners in biomedicine accurately identify and link mentions of diseases and chemicals in text to their corresponding entries in structured knowledge bases like MeSH. You provide raw text data containing medical terms, and it outputs these terms disambiguated and linked to specific MeSH identifiers. This is designed for biomedical natural language processing specialists and researchers working with medical literature or electronic health records.

No commits in the last 6 months.

Use this if you need to precisely map disease and chemical names from unstructured text to a standardized medical vocabulary, improving data consistency and searchability.

Not ideal if your primary domain is outside of biomedicine or if you need to link entities other than diseases and chemicals.

biomedical-nlp medical-entity-linking disease-recognition chemical-recognition knowledge-base-integration
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 8 / 25

How are scores calculated?

Stars

34

Forks

3

Language

Python

License

Last pushed

May 03, 2021

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/izuna385/Entity-Linking-Tutorial"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.