Natural Language Processing (NLP) is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, and text classification and recognition is considered to be an important research field and direction of natural language processing. This paper mainly studies the realization of text classification model through the processing method of text data in natural language processing and the theoretical knowledge and technical means of machine learning. We summarize the existing text classification algorithms, analyze their applicable scenarios, and optimize on the basis of these algorithm models. The paper proposes a Chinese text classification algorithm based on weight preprocessing. Algorithm based on the weight preprocessing link, so that the optimized classifier model can improve the accuracy of the existing text classifier. In this paper, the English Newsgroups corpus is used for experimental verification. The experimental results show that the classification accuracy and accuracy of the improved algorithm are better than those of the traditional text classification algorithm, thereby improving the accuracy of English text classification.
In the era of economic globalization, translation tasks between different languages are becoming more and more frequent, especially English as the most widely used language. The development of information technology has promoted the development of machine translation. Machine translation is faster than human translation. But machine translation also has certain quality problems. So far, machine translation still has problems such as missing translation and poor text translation. In order to solve the current problems, this paper conducts in-depth research on the English translation technology based on machine learning. First, this paper analyzes the principle of machine translation and the Transformer model. In order to improve the quality of machine-translated translations, improvements are proposed to current translation models. This improvement is to optimize the normalization layer of the Transformer model. Based on this improvement, an English translation system based on machine learning is designed. After experimental verification, the improved translation model in this paper can improve the accuracy of translation.
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