Dual-Path Short Text Classification with Data Optimization

In order to solve problems of fragmented information, missing context and difficult-to-capture feature information in short texts, this paper proposes a dual-path classification model combining word-level and sentence-level feature information. Our method is developing the BERT pre-trained model for obtaining word vectors, and presenting attention mechanisms and the BiGRU model to extract local key information and global semantic information, respectively. To tackle the difficulties of models focusing more on hard-to-learn samples during training, a novel hybrid loss function is constructed as an optimization objective, and to address common quality issues in training data, a text data optimization method that integrates data filtering and augmentation techniques is proposed. This method aims to further enhance model performance by improving the quality of input data. Experimental results on three different short text datasets show that our proposed model outperforms existing models (such as Att + BiGRU, BERT + At), with an average F1 score exceeding 90%. Moreover, the performance metrics of the model improved on the datasets optimized with the proposed data optimization method compared to the original datasets, demonstrating the effectiveness of this method in enhancing training data quality and improving model performance.