Named Entity Recognition (NER)

Named Entity Recognition (NER) involves identifying and categorizing entities—a word or set of words that refers to the same thing—in text. NER, therefore, involves two steps: (1) identifying the entity and (2) categorizing it. Examples of entity categories may be Persons, Locations, Times, Monetary Values, etc. The process of assigning a unique identifier to entities is referred to as Named Entity Disambiguation (NED). NER is an application of Natural Language Processing (NLP).

Examples

• NERVE
• Stanford Named Entity Recognizer

Further Resources

• Hooland et al. (2015) “Exploring Entity Recognition and Disambiguation for Cultural Heritage Collections”
• Named Entity Recognition (Wikipedia)
• Selig (2021) “Entity Extraction: How Does It Work?”