An interactive multi-task ESG classification method for Chinese financial texts

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2024-12-20 DOI:10.1007/s10489-024-06068-8
Han Zhang, Yazhou Zhang, Xinyu Wang, Lei Zhang, Lixia Ji
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Abstract

In view of the problems existing in the ESG classification task of Chinese financial texts, such as feature loss caused by excessively long texts, this paper proposes an interactive multi-task model AmultiESG for ESG classification of Chinese financial texts. The model divides Chinese financial text ESG classification and financial sentiment dictionary expansion into primary and secondary tasks. First, BiLSTM model is used to learn the original representation of the text. Then, in the secondary task, the attention mechanism and full connection layers are combined with the domain dictionary to realize the extraction of emotional words. In the main task, in order to prevent feature loss due to the excessively long texts, we process the text again and divide it into blocks according to the period. Meanwhile, we learned new feature representation of the text by combining text label representation, text block representation, BiLSTM output features and domain dictionary features. And we introduce an interactive information transfer mechanism to iteratively improve the predicted results of the two tasks and strengthen the association between them. It has been experimentally demonstrated that the proposed method shows superior performance compared to other baselines for the ESG classification task of Chinese financial text, especially for long-text classification tasks.

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针对中文财经文本 ESG 分类任务中存在的问题,如文本过长导致的特征丢失,本文提出了一种交互式多任务模型 AmultiESG,用于中文财经文本的 ESG 分类。该模型将中文金融文本 ESG 分类和金融情感词典扩充分为主要任务和次要任务。首先,使用 BiLSTM 模型学习文本的原始表示。然后,在次要任务中,将注意力机制和全连接层与领域词典相结合,实现情感词的提取。在主任务中,为了防止过长文本造成的特征丢失,我们对文本进行了重新处理,并按照时间段将文本分成若干块。同时,我们结合文本标签表示、文本块表示、BiLSTM 输出特性和领域词典特征,学习了文本的新特征表示。我们还引入了交互式信息传递机制,以迭代改进两个任务的预测结果,并加强它们之间的关联。实验证明,在中文金融文本的 ESG 分类任务中,特别是在长文本分类任务中,所提出的方法与其他基线方法相比表现出更优越的性能。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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