A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

By Vikas Yadav, Steven Bethard. International Conference on Computational Linguistics, 2018

667 citations recorded by the source corpus.

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/7245e15da2a0f09aeca6c1e7478237985f7e4819

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Citations in the corpus

Span-based Joint Entity and Relation Extraction with Transformer Pre-training (2019, methodology): “While common meth-ods tackle the two problems separately [36, 39, 38], more recent work uses joint models for both steps [3, 21].”
Deep Contextualized Self-training for Low Resource Dependency Parsing (2019, methodology): “, 2017), named entity recognition (Yadav and Bethard, 2018), POS tagging (Plank and Agić, 2018), and machine translation (Vaswani et al.”
Portuguese Named Entity Recognition using BERT-CRF (2019, background): “(2011), neural network NER systems have become popular due to the minimal feature engineering requirements, which contributes to a higher domain independence (Yadav and Bethard, 2018).”
A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “Yadav and Bethard [24] presented a short survey of recent advances in NER based on representations of words in sentence.”
Challenges and Prospects in Vision and Language Research (2019, background): “, named entity recognition (Yadav and Bethard, 2018), sentiment analysis (Zhang et al.”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, background): “Given its importance, NER has attracted much attention in the research community (Yadav and Bethard, 2018).”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “Named Entity Recognition (NER) [130, 177, 270]-based retrieval uses entities in queries and documents for matching, allowing text to be understood at a concept level [141, 268].”