2023
DOI: 10.1162/dint_a_00192
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Relation Extraction Based on Prompt Information and Feature Reuse

Abstract: To alleviate the problem of under-utilization features of sentence-level relation extraction, which leads to insufficient performance of the pre-trained language model and underutilization of the feature vector, a sentence-level relation extraction method based on adding prompt information and feature reuse is proposed. At first, in addition to the pair of nominals and sentence information, a piece of prompt information is added, and the overall feature information consists of sentence information, entity pair… Show more

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Cited by 4 publications
(4 citation statements)
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“…Feature Extraction Layer. We use the representation H from BERT's final layer output as input to the BiGRU [17] model, which enables capturing more semantic information, enhancing token-level representations, and obtaining the vector representation Y:…”
Section: The Model Frameworkmentioning
confidence: 99%
See 2 more Smart Citations
“…Feature Extraction Layer. We use the representation H from BERT's final layer output as input to the BiGRU [17] model, which enables capturing more semantic information, enhancing token-level representations, and obtaining the vector representation Y:…”
Section: The Model Frameworkmentioning
confidence: 99%
“…This is because focal loss assigns more weight to hard samples, making the model pay more attention to them, which is very helpful for this table-filling method. In the fourth group, we replaced the BiGRU [17] model with the BiLSTM model and found that compared to the BiLSTM model, BiGRU worked better in combination with the attention mechanism, resulting in better performance. Figure 4 shows shows the convergence process of various ablation models on the NYT dataset.…”
Section: Ablation Studymentioning
confidence: 99%
See 1 more Smart Citation
“…Ref. [14] proposes a relational extraction method that adds prompt information and feature reuse. Firstly, the prompt information is added before each sentence.…”
Section: Related Workmentioning
confidence: 99%