A parameter sharing model using syntax based graph convolutional neural networks to capture text structure information is proposed to address issues such as error propagation and ignoring inherent relationships between subtasks in pipeline models. This article will specifically introduce a model that combines parameter sharing mode, including the motivation for designing the model, special annotation strategies, and model structure, experimental settings, and analysis of experimental results. Traditional entity relationship extraction was composed of two sub tasks: entity recognition and relationship extraction. When there are errors in entity recognition, the error information will be propagated to the relationship extraction task, leading to error accumulation. In response to this issue, this article proposed a study on joint entity relationship extraction based on graph convolutional neural networks.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.