Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network.

ABSTRACT: Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matri

SUBMITTER: Wang LH 

PROVIDER: S-EPMC10561837 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature