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INTRODUCTIONIntroductionWith the continuous development of technology, artifi...
INTRODUCTIONIntroductionWith the continuous development of technology, artificial intelligence (AI) has gradually become an indispensable part of people's lives. As an important application direction of AI, natural language processing (NLP) technology has also received widespread attention. Among them, text classification is one of the most basic and important tasks in NLP, which is used to classify and classify large amounts of text information.LITERATURE REVIEWLiterature reviewAt present, many scholars have studied text classification, and most of them use machine learning algorithms or deep learning models to classify text. The traditional machine learning algorithm mainly includes decision tree, support vector machine (SVM), K-nearest neighbor (KNN) and so on. With the development of deep learning, many complex network structures such as convolutional neural network (CNN), recurrent neural network (RNN), and attention mechanism are also used for text classification. In addition, some scholars have also tried to combine the advantages of traditional machine learning algorithms and deep learning models to achieve better classification results.METHODOLOGYMethodologyIn this paper, we propose a text classification method based on the attention-enhanced convolutional neural network (CNN) and K-means clustering algorithm. Firstly, we use CNN to extract text features, and then introduce the attention mechanism to automatically learn the weights of different features according to their importance. Finally, we use K-means clustering algorithm to classify text according to the feature vector. In order to verify the effectiveness of the proposed method, we conducted experiments on four benchmark datasets and compared it with other baseline methods. The experimental results show that our method can achieve better classification results.RESULTS AND DISCUSSIONResults and discussionIn this section, we report the experimental results of our method and compare it with several baseline methods on four benchmark datasets. The experimental results show that our method achieves better classification performance in most cases. In order to further verify the effectiveness of our method, we also conduct some additional experiments. The experimental results demonstrate that our method is stable and reliable in the process of practical application.CONCLUSION AND FUTURE WORKSConclusion and future worksIn this paper, we propose a text classification method based on the attention-enhanced convolutional neural network (CNN) and K-means clustering algorithm. Experimental results show that our method can achieve better classification results than baseline methods on benchmark datasets. In addition, we also analyze the influence of different parameters on the classification results, which provides a reference for practical application. Finally, we also point out some limitations of our method and prospects for future work.
