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An Overview of Named Entity Recognition

Authors: Xuezhen Yang; Zhijuan Wang; Xiaobing Zhao; Peng Sun;

An Overview of Named Entity Recognition

Abstract

Named Entity Recognition (NER) is essential for some Natural Language Processing (NLP) tasks. Previous researchers gave a survey of NER in statistical machine learning era, however, research on NER has already changed a lot in recent decade. On the one hand, more and more NER systems adopt deep learning, transfer learning, knowledge base and other methods. On the other hand, multilingual and low resource languages NER researches increase rapidly. To reflect these changes, we here give an overview of NER based on 162 papers of NLP related conferences from 1996 to 2017. In this survey, we discuss two main aspects of NER research - target languages and technical approaches with statistical analysis. Finally, we summarize some conclusions and explore potential future issues in NER research.

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    popularity
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
31
Top 10%
Top 10%
Top 10%
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