A Category Hybrid Embedding Based Approach for Power Text Hierarchical Classification

被引：0
|
作者：
Chen X. [1 ]
Gao P. [1 ]
Liang Y. [1 ]
Ma Y. [1 ]
机构：
[1] School of Control and Computer Engineering, North China Electric Power University, Beijing
来源：
Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis | 2022年 / 58卷 / 01期
关键词：
Category embedding; Hierarchical text classification; Power information technology; Power text classification;
D O I：
10.13209/j.0479-8023.2021.104
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the current power text classification methods ignore the latent semantic association between category labels and therefore lead to low classification performance, a hierarchical multi-label power text classification method is proposed. Firstly, a power multi-label text dataset is built using automatic information extraction based on power unstructured texts, and the hierarchical structural relationships between categories are constructed by leveraging relevant domain knowledge. Secondly, a text classification method HONLSTM-BERT is proposed based on hybrid embeddings of category structure and label semantics for hierarchically classifying power texts in a top-down manner. At last, experiments were made in comparison with some popular text classification methods, and the experimental results show that proposed HONLSTM-BERT method achieves superior classification accuracy, and can efficiently improve the performance of automatic text classification. © 2022 Peking University.
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页码：77 / 82
页数：5
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