Luo, W.; Wang, J.; Yan, X.; Jiang, G. Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction. Sustainability2023, 15, 14942.
Luo, W.; Wang, J.; Yan, X.; Jiang, G. Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction. Sustainability 2023, 15, 14942.
Luo, W.; Wang, J.; Yan, X.; Jiang, G. Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction. Sustainability2023, 15, 14942.
Luo, W.; Wang, J.; Yan, X.; Jiang, G. Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction. Sustainability 2023, 15, 14942.
Abstract
To address the deficiency of existing relation extraction models in effectively extracting relational triplets pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieves an F1-score of 93.3\%, surpassing the baseline model CasRel by 0.8\%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1\% and 92.6\% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5\% and 0.8\%, which highlights the good generalization ability of the proposed model.
Keywords
Relation extraction; Subject feature; Attention mechanism; Railway traffic in Tibet
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
