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19 October 2023 Research on entity relation extraction based on head entity feature extraction
Dezhi An, Xuejie Ma, Cao Jiang
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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270952 (2023) https://doi.org/10.1117/12.2684561
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
During the construction of knowledge graphs, entity relations serve as the basic unit whose extraction effect can greatly affect the precision of knowledge graphs. To avoid the insufficient information extraction of head entities in existing extraction models of entity relation triples, an entity relation extraction model based on head entity feature extraction (HFERE) was proposed in this paper. It first used the RoBERTa (robustly optimized BERT approach) model to perform feature coding to the input sentences and then extract the position of head entities through pointer tagging. After that, it performed the feature enhancement on the head entities through the Bi-LSTM (Bi-directional Long Short-Term Memory) bi-directional current neural network so as to fully utilize the deep information of head entities. The tail entity and the relation between entities were extracted after the head entities were tagged. At the relation extraction stage, the hierarchical pointer tagging was used to avoid the entity overlays. Baidu’s public entity relation extraction data set was used for tests in this paper. The experimental results show that the F1 value of the model on this data set was 3.04% higher than that of the baseline model, which can effectively improve the extraction precision of triples.
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Dezhi An, Xuejie Ma, and Cao Jiang "Research on entity relation extraction based on head entity feature extraction", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270952 (19 October 2023); https://doi.org/10.1117/12.2684561
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KEYWORDS
Head
Feature extraction
Data modeling
Machine learning
Semantics
Neural networks
Deep learning
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