Entity and Relation Joint Extraction Method Based on RoBERTa-Effg-Adv
Entity and relation extraction is a key step in constructing knowledge graph,its purpose is to extract the relation triples in the text.Aiming at the problem that the current Chinese entity relation joint extraction model cannot effectively extract overlapping relation triples and the extraction performance is insufficient,we propose a entity and relation joint extraction model based on RoBERTa-Effg-Adv.At the encoder,the RoBERTa-wwm-ext pre-training model is used to encode the input data,and the Efficient GlobalPointer model is used to process nested and non-nested named entity recognition.The entity and relation triple is split into five tuples for entity and relation joint extraction.Combined with adversarial training,the robustness of the model is improved.In order to obtain machine-readable corpus,the relevant books are scanned,and optical character recognition is performed,and then the relation extraction dataset REDQTTM required by this study is formed by manually labeling the data.The dataset contains 18 entity types and 11 relationship types.The experimental results verify the effectiveness of the proposed method in the task of entity and relation joint extraction in the field of Qu Tan temple murals.The precision on the test set of REDQTTM reaches 94.0%,the recall reaches 90.7%,and the F1 value reaches 92.3%.Compared with the GPLinker model,the precision,recall and F1 value are improved by 2.4%,0.9%and 1.6%respectively.
万方数据
维普
