Text feature extraction based on deep learning: a review.
Text feature extraction based on deep learning: a review.
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DOI：
10.1186/s13638-017-0993-1
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发表时间：
2017
影响因子：
2.6
通讯作者：
Gao Y
中科院分区：
文献类型：
--
作者：
Liang H;Sun X;Sun Y;Gao Y
文本特征项的选择是文本挖掘和信息检索的基础和重要内容。传统的特征提取方法需要手工制作特征。对于手工设计来说，一个有效的特征是一个漫长的过程，但针对新的应用，深度学习能够从训练数据中获得新的有效的特征表示。深度学习作为一种新的特征提取方法，在文本挖掘方面已经取得了一定的成果。深度学习与传统方法的主要区别在于，深度学习是从大数据中自动学习特征，而不是采用手工制作的特征，后者主要依赖于设计者的先验知识，极不可能利用大数据。深度学习可以从大数据中自动学习特征表示，包括数百万个参数。本文首先概述了文本特征提取中常用的方法，然后扩展了文本特征提取中常用的深度学习方法及其应用，并对深度学习在特征提取中的应用进行了展望。
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.