kmdn/combining-linking-techniques

Combining Linking Techniques (CLiT) is an entity linking combination and execution framework, allowing for the seamless integration of EL systems and result exploitation for the sake of system reusability, result reproducibility, analysis and continuous improvement. (We hate waste. Especially wasting time. So let's reuse instead!)

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Experimental

This tool helps researchers and data scientists working with text documents to quickly and efficiently identify and link entities (like people, places, or organizations) within their data. You input plain text, and it outputs annotated documents or hyperlinks to knowledge bases like Wikipedia, showing you exactly what entities were found and where. It's designed for anyone who needs to extract structured information from unstructured text and wants to compare or combine different entity linking approaches.

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Use this if you need to extract and link entities from text, want to compare different entity linking systems, or custom-build a linking pipeline by mixing and matching components.

Not ideal if you're looking for a simple, out-of-the-box solution with no customization or if your primary need is not entity linking.

text-mining information-extraction knowledge-graph-construction nlp-research document-annotation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 8 / 25

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Stars

8

Forks

1

Language

Python

License

MIT

Last pushed

Apr 23, 2024

Commits (30d)

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