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Text and Document Classification Technologies
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the application of machine learning algorithms for multi-label text classification, with an emphasis on techniques such as feature selection, Naive Bayes classifier, K-nearest Neighbor (KNN), hierarchical classification, and support vector machines (SVM). The research covers various aspects of document categorization and information retrieval in the context of text mining and natural language processing.
85
Works
IDs:OpenAlex
How has Text and Document Classification Technologies's publication output changed over time?
ScholarIQpublication output · 2008–2021
Output grew100% over the shown period — from 1 works in 2008 to 2 in 2021.
1
1
1
1
2
3
3
1
2
200820102012201620172018201920202021
What are the most-cited papers on Text and Document Classification Technologies?
ScholarIQmost cited works
HDLTex: Hierarchical Deep Learning for Text Classification
Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber, Laura E. Barnes
2017450 CitationsOPEN ACCESS
Efficient English text classification using selected Machine Learning Techniques
Xiaoyu Luo
S2764413287. 2021236 CitationsOPEN ACCESS
Semi-supervised Multi-label Learning by Solving a Sylvester Equation
Gang Chen, Yangqiu Song, Fei Wang, Changshui Zhang
2008194 Citations
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Sheng Wan, Shirui Pan, Jian Yang, Chen Gong
S4210191458. 2021151 CitationsOPEN ACCESS
Multi-label learning with label-specific features by resolving label correlations
Jia Zhang, Candong Li, Donglin Cao, Yaojin Lin, Songzhi Su, Liang Dai, Shaozi Li
S10169007. 2018102 Citations
Where is Text and Document Classification Technologies research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Text and Document Classification Technologies is open access?
ScholarIQopen access share
53%OPEN ACCESS
Gold
33%
Green
20%
Hybrid
0%
Bronze
0%
Closed
47%
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