andychisholm/nel
Entity linking framework
Quickly and accurately identifies and connects mentions of people, places, or organizations within your text documents to their correct real-world identities, even when ambiguous. It takes raw text as input and outputs text with recognized entities linked to unique identifiers. This is for anyone who needs to make sense of unstructured text, such as researchers analyzing large datasets or content managers organizing information.
180 stars. No commits in the last 6 months.
Use this if you have text documents and need to automatically recognize and link named entities (like 'Apple' referring to the company, not the fruit) to a consistent knowledge base or identifier.
Not ideal if you primarily need to extract sentiment, classify entire documents, or perform simple keyword spotting without resolving entity ambiguity.
Stars
180
Forks
36
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 07, 2018
Commits (30d)
0
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