Abstract
In the process of constructing the knowledge graph, entity recognition and relationship extraction are not only the most fundamental but also the most important tasks, and the effect of their model directly affects the final result of the graph. To establish a more refined knowledge graph, this paper proposes a model of extending Bi-LSTM structural units with Double-word vector and combining Semi-supervised Co-training method. The improved model is used in Chinese named entity recognition and entity-relationship extraction in the Chinese medical field, named Co-Training Double Word embedding conditioned BLSTM (CTD-BLSTM). Experiments show that the CTD-BLSTM model obtains higher accuracy and recall rate than BLSTM in the Chinese medical named entity recognition and entity-relationship extraction. It performs better recognition and adaptability to support the construction of the knowledge graph.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
