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

Abstract

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.

Published at COLING 2018

References in corpus (3)

• Natural Language Processing (almost) from Scratch
• Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
• Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features

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