Multi-dimensional Semantic-based Text Classification Model

In recent years, many excellent text classification models have been proposed by researchers to solve natural language processing tasks. In texts, semantic information at different levels and granularities constitutes the multi-dimensional semantic information of the text, which determines its classification effect. However, existing text classification models have several limitations. Firstly, they cannot extract the multi-dimensional semantic information from the text. Secondly, they cannot determine the weight of the multi-dimensional semantic information on the text's semantic information. To address these limitations, this paper proposes a new deep learning neural network model called the multi-dimensional semantic-based text classification model (MSTCM), which comprises an embedding module, a semantic processing module, and a prediction module. After processing the text through the embedding module, it passes through several sub-modules and layers in the semantic processing module to extract the multidimensional semantic information of the text and its weight on the text's semantic information using the attention mechanism. This information is used to generate a text vector, which is then utilized by the prediction module for text classification. To verify the effectiveness of MSTCM, experiments were conducted on several text classification tasks and compared with various text classification methods. The experimental results demonstrate that MSTCM performs better than other methods on most tasks.