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

Understand

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.