Entity extraction, or named entity recognition (NER), is finding mentions of key “things” (aka “entities”) such as people, places, organizations, dates, and time within text. Entity mentions are the words in text that refer to entities, such as “Franklin Roosevelt,” “White House,” and “U.S.”

Entity resolution (aka, entity linking) takes it one step further and distinguishes between similarly named entities such as John Adams and John Quincy Adams. Or, from the mention of “Roosevelt” figures out within that document if “Roosevelt” refers back to Franklin Roosevelt or Eleanor Roosevelt by looking at the context in which the entity appears — aka coreference resolution.

This feat is possible because entity resolution takes the mention of each entity, looks at the surrounding context, and compares it to a knowledge base (such as Wikidata). For example, if the entity is “Michael Collins,” is he mentioned in the context of “American, NASA, astronaut” or “Ireland, IRA”?

Some entity resolution systems add coreference resolution, where the system chains together mentions of the same person within a document (indoc chaining) or across a body of documents (cross-document chaining), such as:

“Franklin Roosevelt and Eleanor Roosevelt visited Rosebud Diner during his 1940 presidential campaign. Then President Roosevelt commented, “This is the best fried chicken I’ve had in a long time!”

Based on context (modifiers), coreference resolution should figure out that “his” is the same entity as “Franklin Roosevelt” and "then president Roosevelt" refers to Franklin and not Eleanor (who was never president).

More sophisticated coreference resolution will do pronominal resolution and nominal resolution. Pronominal coreference resolution chains named entities to their pronouns. Nominal coreference resolution chains named entities to its noun references.

Type of Coreference Resolution	Example
Named entity	Katherine Johnson’s calculations of orbital mechanics were critical to the success of NASA missions to the moon. Johnson calculated trajectories, launch windows, and emergency return paths.
Pronominal	Katherine Johnson’s calculations of orbital mechanics were critical to the success of NASA missions to the moon. She calculated trajectories, launch windows, and emergency return paths.
Nominal	Katherine Johnson’s calculations of orbital mechanics were critical to the success of NASA missions to the moon. The mathematician calculated trajectories, launch windows, and emergency return paths.

Entity resolution is the linchpin to making entity extraction truly useful. While there may be 16 entity mentions in a document, once the coreference resolution has been completed, there may only be three unique entities! All the downstream analyses around these entities—whether it is detecting sentiment around these entities or adding attributes about the entity to a knowledge base—benefit from having related information linked. Making sure not to link information about similarly named but different entities is equally important.

Disclaimer: All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.

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