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Cited by 373Topic ModelingText and Document Classification TechnologiesAdvanced Text Analysis Techniques

The DOI matches its Crossref record; 122 of the 130 references it cites were found in a registry or an index.

Text feature extraction based on deep learning: a review

Open access
Liang, Hong, Sun, Xiao, Sun, Yunlei, Gao, Yuan·2017·EURASIP Journal on Wireless Communications and Networking
DOI 10.1186/s13638-017-0993-1PMC PMC5732309
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Abstract

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

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Fig. 1 Illustration of RBM
Fig. 1 Illustration of RBMCC BY · PubMed Central
Fig. 2 DBN network structures
Fig. 2 DBN network structuresCC BY · PubMed Central
Fig. 3 RNNs unflods in time
Fig. 3 RNNs unflods in timeCC BY · PubMed Central

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Research works printed but not linked: 9

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Metadata

Authors
Liang, H., Sun, X., Sun, Y., & Gao, Y.
Affiliations
China University of Petroleum, East China (CN)
Corresponding author
Sun, Yunlei
ORCID
Venue
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer Science and Business Media LLC
Published
2017-12
Volume
2017
Issue
1
Pages
211-211
ISSN
  • 1687-1472 (ISSN-L)
  • 1687-1499
DOI
10.1186/s13638-017-0993-1
PMC
PMC5732309
OpenAlex
W2772515592
Cited by
373
References
130
Citations by year
2026: 21 · 2025: 47 · 2024: 57 · 2023: 56 · 2022: 78 · 2021: 43 · 2020: 41 · 2019: 19 · 2018: 11
FWCI
2.82
Type
journal article
Language
en
Update notices
correction (2018-02-16)
Open access
Licence
Locations
  • EURASIP Journal on Wireless Communications and Networking (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Fundamental Research Funds for the Central Universities (Grant No.18CX02019A)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
Keywords
text feature extraction, deep learning, automatic feature learning, feature representation, text mining, information retrieval, handcrafted features, big data

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