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Text feature extraction based on deep learning: a review.

Hong Liang¹, Xiao Sun¹, Yunlei Sun¹

¹College of Computer and Communication Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Huangdao District, Qingdao, 266580 China.

EURASIP Journal on Wireless Communications and Networking

|December 22, 2017

Summary

This summary is machine-generated.

Deep learning automatically extracts text features from big data, overcoming limitations of traditional handcrafted methods. This approach enhances text mining and information retrieval by learning effective representations.

Keywords:

Deep learning Feature extraction Natural language processing Text characteristic Text mining

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Area of Science:

Natural Language Processing
Machine Learning
Data Science

Background:

Feature extraction is crucial for text mining and information retrieval.
Traditional methods rely on handcrafted features, which are time-consuming and limited by domain expertise.
These conventional approaches struggle to leverage the full potential of big data.

Purpose of the Study:

To explore common text feature extraction methods.
To detail deep learning techniques for automatic feature representation in text mining.
To forecast future applications of deep learning in this domain.

Main Methods:

Review of conventional text feature extraction techniques.
In-depth analysis of deep learning models for feature learning.
Examination of deep learning applications in text mining tasks.

Main Results:

Deep learning automatically learns effective text feature representations from large datasets.
This contrasts with traditional methods that require manual feature engineering.
Deep learning models can capture complex patterns and nuances in text data.

Conclusions:

Deep learning offers a powerful, data-driven alternative to handcrafted features in text mining.
Automatic feature learning significantly improves performance and scalability.
The adoption of deep learning is poised to transform feature extraction in information retrieval and related fields.