Named Entity Recognition (NER) is often used to identify key people, places, and things within a Content Set. 

NER is a natural language processing method that seeks to identify and classify each term in the Content Set as specific entity categories or "classes". The model underpinning the Named Entity Recognition tool uses statistical patterns and textual context to predict entities within texts.   

One common use case is to download the data from the Named Entity Recognition tool and use it to compile the names of countries, cities, states, buildings, etc, which can then be plotted using GIS (mapping) software. 

For more information about Named Entity Recognition in Gale Digital Scholar Lab, click here. 

PROJECTS

The Books He Carried: A Study of Lindsley Foote Hall's Reading Habits on His Travels | Julianne Peeling (University of Washington)  

Of Christ and Capital: The 'Sunday Question' in the 1893 Columbian Exposition | Marie Peeples, Ian Reinl, Elise Tomasian, Danielle Worthy (University of Washington)

Welcome to the Digital Serpent Lab | Sid, Truc, Karen, Adelina & Courtney (University of Washington) 

A Digital Historiography of treaties and disputes between the coast Salish tribes and U.S. government | Marie Christine O'Connell (University of Washington)

Sample Project 3: Food and Civility 1650-1800