A Survey on Deep Learning for Named Entity Recognition

By Jing Li, Aixin Sun, Jianglei Han, Chenliang Li. IEEE Transactions on Knowledge and Data Engineering, 2018

1,494 citations recorded by the source corpus.

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/16c0ef924da1f6b510c9c783ac764156f5a3d631

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In Artificial Intelligence

Citations in the corpus

A Survey on Intent-Based Networking (2023, background): “In this case, the most useful named entities will be extracted and classiﬁed into predetermined categories, such as names, organization, location, etc. [54].”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, positive): “These models aggregate contextual embeddings according to the input, and context encoders then output word type tags through tag decoders such as a CRF structure or a softmax structure [99].”
The Computational Limits of Deep Learning (2020, background): “Shortly thereafter, deep learning systems also won at object detection [25, 26, 27], named-entity recognition [28], machine translation [29, 30, 31], question answering[32], and speech recognition [33, 34].”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “[21] provides an extensive discussion on recent deep learning methods for named entity recognition.”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, background): “…of many information extraction tasks including entity linking (Derczynski et al., 2015), opinion mining (Maynard et al., 2012), and event detection (Ritter et al., 2012), named entity recognition (NER) has attracted much attention in the research community in the past two decades (Li et al., 2018).”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “Retrieval with Named Entity Recognition.”