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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.
Highlights
Machine learning is a branch of artificial intelligence, and in many cases, almost becomes the pronoun of artificial intelligence
The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than the state-of-the-art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method [82]
In Reference [112], this study develops a total cost of ownership (TCO) model for flash storage devices and plugs a Write Amplification (WA) model of NVMe SSDs we build based on the empirical data into this TCO model
Summary
Machine learning is a branch of artificial intelligence, and in many cases, almost becomes the pronoun of artificial intelligence. Machine learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search [1]. These applications that are made to use of a class of techniques are called deep learning [1, 2]. Representation learning is a set of methods that allow a machine to be given with raw data and to automatically
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