Entity relation extraction model based on probabilistic threshold decay

Junkang Zheng; Jun Yang; Jiahua Zhang

doi:10.1117/12.2631039

18 March 2022 Entity relation extraction model based on probabilistic threshold decay

Junkang Zheng, Jun Yang, Jiahua Zhang

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121680U (2022) https://doi.org/10.1117/12.2631039
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

Omission of entities in the extraction process is caused by the model that performs joint extraction of entity relations by estimating the initial and last positions of entities. The probability threshold for predicting entity positions is dynamically altered in this work by modifying the probability threshold for judging entity positions. This reduces the loss of subject entities in the prediction process based on assessing entity positions using the proximity principle. Furthermore, different weights are provided to distinct entity relations to optimize the model's effect on entity relation extraction with few samples and raise extraction accuracy. Experiments show that, when compared to the existing algorithm, the algorithm can reduce the probability of the model missing subject entities during the extraction process, and the model's accuracy improves by nearly 0.2% to 0.5% in a small number of samples extracted using random sampling. The accuracy of model extraction can be improved by altering the weight assignment function, and the experimental results have improved by approximately 1%.

Citation Download Citation

Junkang Zheng, Jun Yang, and Jiahua Zhang "Entity relation extraction model based on probabilistic threshold decay", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121680U (18 March 2022); https://doi.org/10.1117/12.2631039

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