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Text feature extraction based on deep learning: a review
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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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Metadata
- Authors
- Liang, H., Sun, X., Sun, Y., & Gao, Y.
- Affiliations
- China University of Petroleum, East China (CN)
- Corresponding author
- Sun, Yunlei
- Venue
- EURASIP Journal on Wireless Communications and Networking
- Published
- 2017-12
- Volume
- 2017
- Issue
- 1
- Pages
- 211-211
- ISSN
- 1687-1472 (ISSN-L)
- 1687-1499
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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