A Survey on Deep Learning for Named Entity Recognition

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,...

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Vydané v:IEEE transactions on knowledge and data engineering Ročník 34; číslo 1; s. 50 - 70
Hlavní autori: Li, Jing, Sun, Aixin, Han, Jianglei, Li, Chenliang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Annotations
Coders
Deep learning
Encyclopedias
Human engineering
Machine translation
named entity recognition
Natural language processing
Recognition
Representations
Semantics
survey
Task analysis
Taxonomy
Text recognition
ISSN:1041-4347, 1558-2191
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.2981314