Research On Joint Entity Relation Extraction Based On Deep Learning With Pointer Annotation

The goal of entity relation extraction task is to predict entities and their relationships from unstructured text,and then convert it into structured data,which is one of the core technologies for building knowledge bases.Facing the overlapping relationship problem in complex contexts,the current mainstream research mainly adopts a two-step joint relation extraction method.Although these methods have achieved a certain degree of success,they still face the issues of exposure error and insufficient head-tail entity interaction,which affect the performance of relation extraction.In response to these challenges,this paper propose corresponding methods and achieves significant results.The main contributions include:Firstly,this paper propose a pointer-labeling joint relation extraction model based on global features.The model employs a pointer-labeling framework,which effectively tackles overlapping relationship issues in complex contexts.Simultaneously,by encoding sentences with BERT,and combining multi-head attention mechanism and multi-kernel convolutional neural networks to extract multi-granular global features for guiding the prediction of head entities,it helps to improve the accuracy of head entity recognition and reduce exposure errors.Secondly,building upon the aforementioned relation extraction model,this paper designs an adaptive relation-entity information fusion module and proposes a joint relation extraction model that integrates both relation and entity information.The adaptive relation-entity information fusion module consists of a relation attention module and two gated fusion modules.The relation attention module is responsible for establishing the interaction between entities and relations,obtaining the importance of different relationships for each word,while the two gated fusion modules combine relation embeddings and head entity information with each word embedding,guiding the prediction of relations and tail entities,and strengthening the interaction between the head and tail entity tasks.Lastly,in order to verify the effectiveness of the proposed methods,this paper conducts baseline model comparison experiments,ablation experiments,and evaluation experiments for overlapping issues on the classic NYT and Web NLG datasets.The experimental results show that the final model proposed in this paper achieves an F1 score of 91.6% on the NYT dataset and 92.6% on the Web NLG dataset.demonstrating These results demonstrate that the model proposed in this paper has a significant advantage in addressing relationship overlapping issues.