A Brief History of Named Entity Recognition

TL;DR

This paper surveys the evolution of Named Entity Recognition (NER) techniques over three decades, highlighting the shift from supervised to unsupervised learning methods and their impact on information extraction.

Contribution

It provides a comprehensive overview of NER's development, comparing various techniques and their effectiveness over time.

Findings

Supervised methods dominated early NER approaches.

Unsupervised learning methods are increasingly prominent.

Evolution has improved NER accuracy and applicability.

Abstract

A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. More concretely, it is identifying and classifying entities in the text that are crucial for Information Extraction, Semantic Annotation, Question Answering, Ontology Population, and so on. The process of NER has evolved in the last three decades since it first appeared in 1996. In this survey, we study the evolution of techniques employed for NER and compare the results, starting from supervised to the developing unsupervised learning methods.