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A Brief History of Named Entity Recognition

Monica Munnangi

TL;DR

In this survey, the evolution of techniques employed for NER is studied and the results are compared, starting from supervised to the developing unsupervised learning methods.

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.

A Brief History of Named Entity Recognition

TL;DR

In this survey, the evolution of techniques employed for NER is studied and the results are compared, starting from supervised to the developing unsupervised learning methods.

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.

Paper Structure

This paper contains 9 sections, 12 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Data-sets and Tools
  3. Evaluation Metrics
  4. Background
  5. Rule-based methods
  6. Feature Extraction Methods
  7. State-of-the-Art Systems
  8. Unsupervised and Beyond
  9. Future Directions

Figures (8)

  • Figure 1: Example Named Entity Recognition task DBLP:journals/corr/abs-1812-09449
  • Figure 2: Named Entity Recognition on New York Times dataset
  • Figure 3: Confusion Matrix
  • Figure 4: An Example AUC - ROC Curve
  • Figure 5: Rule Based NER as described in chiticariu-etal-2010-domain
  • ...and 3 more figures