Large⁃scale semantic text overlapping region retrieval based on deep learning

As a hot topic in natural language processing, overlapping region recognition needs to be further explored and studied. Aiming at the problem of poor accuracy and recall in traditional text overlapping region retrieval methods, a large-scale semantic text overlapping region retrieval method based on deep learning is proposed. Combined with sparse automatic encoder and depth confidence network, a hybrid model is constructed. According to the hybrid model, a text classifier is designed and constructed. The main components of the classifier are text preprocessing, feature learning and classification retrieval. In this paper, a series of preprocessing, such as de-noising, word segmentation and stop word removal, are carried out. Finally, softmax regression is used to realize text classification, and the learned text features are used as the input of the classifier to get the classification and retrieval results of the overlapping regions. The experimental results show that the accuracy and recall of the method are both high, showing reliability and robustness. © 2021, Jilin University Press. All right reserved.