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Active learning strategies for extracting phrase-level topics from scientific literature
Tao Yue
*
,
Yu Li
, Zhang Runjie
*
此作品的通讯作者
CAS - National Science Library
University of Chinese Academy of Sciences
State Key Laboratory of Resources and Environmental Information System
University of Southampton
科研成果
:
期刊稿件
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同行评审
2
引用 (Scopus)
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探究 'Active learning strategies for extracting phrase-level topics from scientific literature' 的科研主题。它们共同构成独一无二的指纹。
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Computer Science
Active Learning
100%
Scientific Literature
100%
Bidirectional Long Short-Term Memory Network
25%
Convolutional Neural Network
25%
Neural Network Model
25%
Semantic Label
25%
Annotation
25%
Extracting Information
25%
Corpus Construction
25%
Sentence Length
25%
Social Sciences
Learning Strategy
100%
Scientific Literature
100%
Neural Network
25%
Labor Cost
25%
Mathematics
Neural Network
100%
Network Model
100%
Arts and Humanities
Active Learning
100%
reliance
25%