Text feature extraction based on deep learning: a review

By Hong Liang, Xiao Sun, Yunlei Sun, Yuan Gao. EURASIP Journal on Wireless Communications and Networking, 2017

333 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4c0939ba18557f494dfee5fbb5e6a524daa9d6ff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, background): “Moreover, although existing natural language processing (NLP) techniques can process large text corpora, they require extensive feature engineering (Liang et al. 2017; Young et al. 2018), which is not scalable to multi-category content.”
Text Classification Algorithms: A Survey (2019, background): “The main idea is that one hidden layer between input and output layers has fewer units [93] and could thus be used to reduce the dimensions of a feature space.”