Entity linking

In natural language processing, Entity Linking, also referred to as named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD), named-entity normalization (NEN)[1], or Concept Recognition, is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the main idea is to first identify "Paris" and "France" as named entities, and then to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris" and "France" to the french country.

The Entity Linking task is composed of 3 subtasks.

  1. Named Entity Recognition: Extraction of named entities from a text.
  2. Candidate Generation: For each named entity, select possible candidates from a Knowledge Base (e.g. Wikipedia, Wikidata, DBPedia, ...).
  3. Disambiguation: Choose the correct entity from this set of candidates.


In entity linking, each named entity is linked to a unique identifier. Often, this identifier corresponds to a Wikipedia page.
  1. ^ Cite error: The named reference khalid2008 was invoked but never defined (see the help page).