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A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods

Sakher Khalil Alqaaidi, Elika Bozorgi, Afsaneh Shams, Krzysztof Kochut

TL;DR

A survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance is presented.

Abstract

Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. Our survey is helpful for researchers in knowing the recent techniques in text mining and extracting structured information from raw text.

A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods

TL;DR

A survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance is presented.

Abstract

Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. Our survey is helpful for researchers in knowing the recent techniques in text mining and extracting structured information from raw text.
Paper Structure (15 sections, 1 equation, 4 tables)

This paper contains 15 sections, 1 equation, 4 tables.

Table of Contents

  1. Introduction
  2. Benchmarks
  3. Named Entity Recognition Benchmarks
  4. Relation Classification Benchmarks
  5. Relation Extraction Benchmarks
  6. Methodology
  7. Unified NER and RC Models
  8. Named Entity Recognition Models
  9. Comprehensive NER Models
  10. Flat NER Models
  11. Document-level Models
  12. Few-shot NER Models
  13. Relation Classification
  14. Few-shot RC Models
  15. Conclusion