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entity-linking

Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.

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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!)

  • Updated Apr 23, 2024
  • Python

This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).

  • Updated Mar 16, 2024
  • Python

Created by Halbert L. Dunn

Released 1946

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artificial-intelligence nlp