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.

引用

页码：77 / 82

页数：5

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