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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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 34; číslo 1; s. 50 - 70
Hlavní autoři:	Li, Jing, Sun, Aixin, Han, Jianglei, Li, Chenliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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.
AbstractList	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.
Author	Li, Jing Han, Jianglei Li, Chenliang Sun, Aixin
Author_xml	– sequence: 1 givenname: Jing orcidid: 0000-0002-3262-3734 surname: Li fullname: Li, Jing email: jingli.phd@hotmail.com organization: Inception Institute of Artificial Intelligence, Abu Dhabi, UAE – sequence: 2 givenname: Aixin orcidid: 0000-0003-0764-4258 surname: Sun fullname: Sun, Aixin email: axsun@ntu.edu.sg organization: School of Computer Science and Engineering, Nanyang Technological University, Singapore – sequence: 3 givenname: Jianglei surname: Han fullname: Han, Jianglei email: ray.han@sap.com organization: SAP, Singapore – sequence: 4 givenname: Chenliang orcidid: 0000-0003-3144-6374 surname: Li fullname: Li, Chenliang email: cllee@whu.edu.cn organization: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
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Snippet	Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location,...
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SubjectTerms	Annotations Coders Deep learning Encyclopedias Human engineering Machine translation named entity recognition Natural language processing Recognition Representations Semantics survey Task analysis Taxonomy Text recognition
Title	A Survey on Deep Learning for Named Entity Recognition
URI	https://ieeexplore.ieee.org/document/9039685 https://www.proquest.com/docview/2607877432
Volume	34
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