Research and Application of Machine Learning Algorithm in Natural Language Processing and Semantic Understanding

In recent years, with the rapid development of machine learning technology, especially the wide application of deep learning methods, new breakthroughs have been made in the fields of natural language processing (NLP) and semantic understanding. Machine learning algorithm can automatically learn rules from a large number of data and effectively deal with the complexity and diversity of language, thus significantly improving the accuracy of semantic understanding. In this study, a deep learning model based on Transformer architecture is adopted in NLP and semantic understanding tasks, and self-attention mechanism is combined to capture long-distance dependencies in texts. The experimental results show that the performance of different models is equivalent and the accuracy is at a high level when dealing with short texts. However, when dealing with medium and long texts, the performance differences between the models begin to appear, especially the Transformer model has obvious advantages in dealing with long texts, and its accuracy is obviously higher than other models. This discovery confirms the effectiveness of Transformer model in capturing long-distance dependencies. This study reveals the important position of Transformer model in NLP and semantic understanding through systematic experimental design and data analysis. These findings provide valuable reference for the research in related fields, and also provide guidance for model selection in practical application.