Semantic Discovery of Online Health Information Based on Improved CasRel Entity-Relationship Extraction Model
[Objective]This paper aims to achieve semantic discovery and relation extraction from a large amount of complex user-generated information from an online healthcare platform.[Methods]First,we constructed a semantic discovery model for online health information based on an improved CasRel model.Then,we introduced the ERNIE-Health pre-trained model,which is more suitable for the healthcare domain,into the text encoding layer of the CasRel-based model.Finally,we used a multi-level pointer network in the entity and relation decoding layer to annotate and fuse subject features for relations and object decoding via neural networks.[Results]Compared to the original model,the improved CasRel entity-relation extraction model increased the F1-scores of entity recognition and entity-relation extraction tasks for online health information semantic discovery by 7.62％and 4.87％,respectively.[Limitations]The overall effectiveness of the model still needs to be validated with larger datasets and empirical studies on health information from different disease types.[Conclusions]Three sets of comparative experiments validated the effectiveness of the improved CasRel entity-relation extraction model for online diabetes health information semantic discovery tasks.
Online Health InformationEntities ExtractionRelationship ExtractionSemantic Discovery
NETL
NSTL
万方数据
