Research on the knowledge elements extraction method in aviation domain based on head-tail pointers

Haixiang Yang; Xindong You; Qin Zhang; Xueqiang Lv

doi:10.1117/12.3004664

9 October 2023 Research on the knowledge elements extraction method in aviation domain based on head-tail pointers

Haixiang Yang, Xindong You, Qin Zhang, Xueqiang Lv

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912C (2023) https://doi.org/10.1117/12.3004664
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Traditional relationship extraction requires manually labeling a small number of relationships and corresponding entities in a specific field as the training set. However, when switching to a new scene, it is necessary to redefine the rules and relabel the data. This will consume a lot of resources. With the continuous change of data types in the aviation field, it is necessary to design a general entity-relationship extraction model. For the research of open relation extraction in aviation field, we propose an open knowledge element extraction model based on Roberta + head-tail recognizer called RHTR. We constructed and made public a knowledge meta-corpus in the field of aviation. The model inputs the source text into Roberta to obtain the corresponding word embedding. The embedding is fed to the head-tail recognizer to do twice five classifications to get the head and tail sequence. Matching to the position information in the sentence based on the head and tail sequence, so as to get the corresponding label information. Then the label information is matched using the proximity principle to obtain the knowledge elements of the target. RHTR achieves 85.0% precision and 80.6% recall on the aviation field corpus. This fully verifies the superiority of the model. RHTR can obtain more kinds of knowledge elements while ensuring the accuracy of knowledge elements.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haixiang Yang, Xindong You, Qin Zhang, and Xueqiang Lv "Research on the knowledge elements extraction method in aviation domain based on head-tail pointers", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912C (9 October 2023); https://doi.org/10.1117/12.3004664

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KEYWORDS

Education and training

Missiles

Data modeling

Head

Feature extraction

Mathematical optimization

Adversarial training

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