AdirthaBorgohain/NER-RE

A Named Entity Recognition + Entity Linker + Relation Extraction Pipeline built using spacy v3. Given a text, the pipeline will extract entities from the text as trained and will disambiguate the entities to its normalized form through an Entity Linker connected to a Knowledge Base and will assign a relation between the entities, if any.

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Emerging

This tool helps you automatically extract key pieces of information from unstructured texts like contracts or medical records. You feed it raw text, and it identifies specific entities (like names or dates), clarifies what they refer to using a knowledge base, and then maps out how these entities are related to each other. It's designed for data analysts, researchers, or anyone who needs to quickly make sense of large volumes of text data and build structured insights.

No commits in the last 6 months.

Use this if you need to automatically identify key entities and their relationships within large collections of text documents, such as legal contracts, scientific papers, or financial reports.

Not ideal if you only need simple keyword extraction or if your text data is already highly structured and doesn't require complex entity linking or relationship mapping.

information-extraction legal-tech healthcare-informatics financial-document-analysis knowledge-graph-building
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 8 / 25
Community 17 / 25

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Stars

44

Forks

10

Language

Python

License

Last pushed

Jun 02, 2023

Commits (30d)

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