基于跨领域迁移的AM-AdpGRU金融文本分类

doi:10.3969/j.issn.0255-8297.2022.05.012

Abstract

Abstract: Aiming at the problem that the current financial text classification model based on deep learning heavily depends on labeled data, this paper proposes an am AM-AdpGRU financial text classification model based on cross domain migration, which migrates related domain data to the target domain data by learning the classification criteria of the data. The am AM-AdpGRU model first uses deep network adaptation to overcome the migration loss caused by the difference of data distribution between the source domain and the target domain, so that the model does not need to be reconstructed even when the data distribution changes; Then, the feature selection principle of the target domain to the source domain is established by using attention mechanism, so that the model's attention to the source domain can focus on the part with higher similarity with the target domain. Experiments are carried out on the open cross domain emotion review Amazon dataset and semeval-2017 microblog financial dataset, and the am AM-AdpGRU model is compared with other methods. Experimental results show that the average classification accuracy of am AM-AdpGRU model is significantly improved compared with other models.

Key words: financial text classification, cross-domain migration, deep network adaptation, source domain, target domain, feature selection mechanism

CLC Number:

TP301

WU Feng, XIE Cong, JI Shaopei. AM-AdpGRU Financial Text Classification Based on Cross-Domain[J]. Journal of Applied Sciences, 2022, 40(5): 828-837.

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AM-AdpGRU Financial Text Classification Based on Cross-Domain

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