Research on entity relations extraction based on deep learning

Abstract

The Internet has brought strong scientific and technological support to social development. With the hardware development rapidly in information technology field, such as digital devices, the application demand of Internet knowledge services by network users has been highly valued by scholars. Entity relation extraction technology is an important technical support for various search engines and machine response artificial intelligence applications, which can help users extract knowledge from massive text data on the Internet. This article use deep learning to study how to enhance the effectiveness and accuracy of entity relationship extraction. Firstly, the basic structure, algorithm principle and process of the most important basic algorithm in neural network are described. The main ways and types of building in-depth model in entity relationship extraction are discussed. After that, the concept of attention-based entity relationship extraction model is put forward, and the structure and implementation method of the model are analyzed. The scheme of improving the model to promote the accuracy and the learning rate. The experimental results indicate that optimization scheme improves the model learning rate and improves the effectiveness of entity phrase pre-training. Compared with other in-depth learning methods, the optimized in-depth learning model proposed can identify the weight of local features more accurately, thus further improving the judgment of the relationship between entity semantics. The model research results text entity relationship extraction have excellent prospect in the field of Web Text Knowledge Service application.