Abstract
Among many natural language processing tasks, the task of relation classification is an important basic work. Relational classification is required to provide high-quality corpus in fields such as machine translation, structured data generation, knowledge graphs, and semantic question answering. Existing relational classification models include models based on traditional machine learning, models based on deep learning, and models based on attention mechanisms. The above-mentioned models all have the disadvantage of only using a single feature for relationship classification, and do not effectively combine entity features and context features. Based on the above problems, this study proposes a relational classification network that integrates multi-scale semantic features. Through the combination of entity features and context semantic features, the model can better learn the relationship features between entities. In addition, in order to verify the validity of the model and the effectiveness of each module, this study conducted experiments on the SemEval-2010 Task 8 and KBP37 data sets. Experimental results demonstrate that the model performance is higher than most existing models.
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Acknowledgements
This work was supported by National Key Research and Development Program of China (2022YFB4004401), and the Taishan Scholars Program (NO. tsqn202103097).
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Li, G., Tian, J., Zhou, M., Li, M., Han, D. (2023). A Relational Classification Network Integrating Multi-scale Semantic Features. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14303. Springer, Cham. https://doi.org/10.1007/978-3-031-44696-2_11
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DOI: https://doi.org/10.1007/978-3-031-44696-2_11
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