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.

arXiv commentPublished at COLING 2018

References in corpus (5)

• Natural Language Processing (almost) from Scratch
• Character-Aware Neural Language Models
• Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
• Multi-Task Cross-Lingual Sequence Tagging from Scratch
• Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features

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• Neural Entity Linking: A Survey of Models Based on Deep Learning
• Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
• Zero-shot cross-lingual transfer language selection using linguistic similarity
• Comparative Analysis of Word Embeddings for Capturing Word Similarities
• FarsTail: A Persian Natural Language Inference Dataset
• CrossNER: Evaluating Cross-Domain Named Entity Recognition
• From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts
• Named entity recognition architecture combining contextual and global features
• Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
• A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
• Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition
• Reddit Entity Linking Dataset
• TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
• Entity Recognition from Colloquial Text
• An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
• Literature review on vulnerability detection using NLP technology
• The Use of Artificial Intelligence in Military Intelligence: An Experimental Investigation of Added Value in the Analysis Process
• Targeted Sentiment Analysis: A Data-Driven Categorization
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• From Academia to Software Development: Publication Citations in Source Code Comments
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• Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts
• Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform
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• Knowledge Guided Named Entity Recognition for BioMedical Text
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• Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
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• Neural Correction Model for Open-Domain Named Entity Recognition
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• Classification of Contract-Amendment Relationships
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• Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
• Sentence Structure and Word Relationship Modeling for Emphasis Selection
• Active Learning for Product Type Ontology Enhancement in E-commerce
• Costs to Consider in Adopting NLP for Your Business
• Named Entity Recognition -- Is there a glass ceiling?
• Deep Contextualized Self-training for Low Resource Dependency Parsing
• Building and Evaluating Universal Named-Entity Recognition English corpus