Deep Learning Based Chinese Entity Relaton Extraction Research
Entity relation extraction is important for building large-scale knowledge graphs,as it allows us to extract factual triplets from text.Existing research typically performs entity recog-nition first,followed by relation classification or unified annotation to complete this task.While these methods make relation extraction more achievable and flexible,they also suffer from error accumulation and error exposure.Additionally,they cannot handle difficult problems such as o-verlapping relations and nested entities.To address these issues,this paper proposes a deep learn-ing-based Chinese entity relation joint extraction model.he model consists of a score-based clas-sifier,a specific relation role labeling strategy,and a partition filter.Firstly,the partition filter divides the input text into three partitions:entity partition,relation partition,and shared parti-tion,which ensures better bidirectional interaction between entity recognition and relation ex-traction tasks.Then,the specific relation role labeling strategy is applied to decode the entity in-formation,and finally,a score-based classifier outputs factual relation triplets.Experiments show that the proposed method can improve the problems of error accumulation and interaction omission,as well as entity redundancy,which is commonly seen in traditional methods.The ac-curacy of triplet extraction is improved as a result.
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