Feature Engineering For Text Data
Feature Engineering for Text Data is a domain-specific feature design methodology that systematically transforms raw textual inputs into structured numerical representations through techniques such as tokenization, vectorization, and syntactic or semantic feature extraction to enhance machine learning model performance.
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Publications
18.4K
Citations
1.2K
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469
Institutions
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2017–2026
184
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Text Classification Algorithms: A Survey
Information · 2019 · 1.3K citations · Full text
Text Classification using String Kernels
Huma Lodhi, John Shawe‐Taylor, Nello Cristianini et al. · Bristol Research (University of Bristol) · 2000 · 514 citations · Full text
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Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
19 publications
563 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
16 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
15 publications
437 citations
KU Leuven
Leuven, Belgium
4 h-index
7 publications
410 citations

Peking University
Beijing, China
4 h-index
18 publications
331 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
13 publications
842 citations
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
4 h-index
19 publications
909 citations

University of Edinburgh
Edinburgh, United Kingdom
4 h-index
5 publications
345 citations
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9 publications · 819 citations
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Repository · 9 publications · 626 citations
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9 publications · 320 citations
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6 publications · 570 citations
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5 publications · 204 citations
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