Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language

• Main Authors: Dongyang Wang, Junli Su, Hongbin Yu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Feature extraction; English word segmentation processing; long short term memory; gated recurrent unit
• Online Access: https://ieeexplore.ieee.org/document/8999624/

NLP (Natural Language Processing) is a technology that enables computers to understand human languages. Deep-level grammatical and semantic analysis usually uses words as the basic unit, and word segmentation is usually the primary task of NLP. In order to solve the practical problem of huge structural differences between different data modalities in a multi-modal environment and traditional machine learning methods cannot be directly applied, this paper introduces the feature extraction method of deep learning and applies the ideas of deep learning to multi-modal feature extraction. This paper proposes a multi-modal neural network. For each mode, there is a multilayer sub-neural network with an independent structure corresponding to it. It is used to convert the features in different modes to the same-modal features. In terms of word segmentation processing, in view of the problems that existing word segmentation methods can hardly guarantee long-term dependency of text semantics and long training prediction time, a hybrid network English word segmentation processing method is proposed. This method applies BI-GRU (Bidirectional Gated Recurrent Unit) to English word segmentation, and uses the CRF (Conditional Random Field) model to annotate sentences in sequence, effectively solving the long-distance dependency of text semantics, shortening network training and predicted time. Experiments show that the processing effect of this method on word segmentation is similar to that of BI-LSTM-CRF (Bidirectional- Long Short Term Memory-Conditional Random Field) model, but the average predicted processing speed is 1.94 times that of BI-LSTM-CRF, effectively improving the efficiency of word segmentation processing.