Natural Language Processing with Improved Deep Learning Neural Networks

• Published in: Scientific programming Vol. 2022; pp. 1 - 8
• Main Author: Zhou, YiTao
• Format: Journal Article
• Language: English
• Published: New York Hindawi 07.01.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Back propagation; Classifiers; Datasets; Deep learning; Feature extraction; Knowledge bases (artificial intelligence); Language; Natural language processing; Neural networks; Sentences; Syntax
• DOI: 10.1155/2022/6028693
• ISSN: 1058-9244

As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.