Application Optimization of NLP System under Deep Learning Technology in Text Semantics and Text Classification
Gang Jin
Abstract
How to use advanced deep learning technology to build an effective and powerful text classification model, extract text semantic attributes and achieve good classification results on large-scale test datasets has always been the main problem and severe challenge of text classification tasks. The purpose of this paper is to study the application optimization of NLP system in text semantics and text classification based on deep learning technology. A WC-GCN classification algorithm combining GCN and Attention mechanism is proposed. The related tasks of text classification are discussed, including three typical text classification tasks, including sentiment analysis, topic analysis, and news classification. The algorithm model proposed in this paper is verified from the experimental point of view. Compared with other deep learning-based text classification methods, the proposed algorithm model performs better on the public datasets of three text classification tasks. The news text also achieved a high accuracy rate.