MAL: multilevel active learning with BERT for Chinese textual affective structure analysis

Shufeng XIONG; Guipei ZHANG; Xiaobo FAN; Wenjie TIAN; Lei XI; Hebing LIU; Haiping SI

doi:10.1631/FITEE.2400242

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MAL: multilevel active learning with BERT for Chinese textual affective structure analysis

Regular Papers | Updated：2025-07-02

- MAL: multilevel active learning with BERT for Chinese textual affective structure analysis
  Enhanced Publication
- MAL：基于BERT的多层次主动学习用于中文文本情感结构分析
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 6, Pages: 833-846(2025)
- Affiliations：
  
  College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
- Author bio：
  
  E-mail: xsf@whu.edu.cn
  ‡ Corresponding author
- Funds：
- DOI：10.1631/FITEE.2400242
  CLC： TP311
- Received：31 March 2024，
  
  Revised：2024-09-22，
  
  Published：2025-06
- Accepted：
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Shufeng XIONG, Guipei ZHANG, Xiaobo FAN, et al. MAL: multilevel active learning with BERT for Chinese textual affective structure analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(6): 833-846.
DOI：

Shufeng XIONG, Guipei ZHANG, Xiaobo FAN, et al. MAL: multilevel active learning with BERT for Chinese textual affective structure analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(6): 833-846. DOI： 10.1631/FITEE.2400242.

摘要

中文文本情感结构分析（CTASA）是一项序列标注任务，通常依赖于带监督的深度学习方法。然而，获取用于训练的大型带标注数据集可能既昂贵又耗时。通过选择最有价值的样本，主动学习提供了一种降低标注成本的解决方案。以往的主动学习方法主要关注样本的不确定性或多样性，但实际上还面临着模型偏差或选择到无关样本等挑战。为解决这些问题，本文引入多层次主动学习（MAL），该方法利用句子和词汇两个层面的深层文本信息建模汉语的复杂结构。通过整合从基于Transformer的双向编码器表示（BERT）嵌入中提取的句子级特征以及从随机条件场（CRF）模型中获得的词汇级概率分布，MAL能够全面捕捉中文文本的情感结构（CTAS）。实验结果表明，与基线方法相比，MAL显著降低了约70％的标注成本，并且性能更加稳定。

Abstract

Chinese textual affective structure analysis (CTASA) is a sequence labeling task that often relies on supervised deep learning methods. However

acquiring a large annotated dataset for training can be costly and time-consuming. Active learning offers a solution by selecting the most valuable samples to reduce labeling costs. Previous approaches focused on uncertainty or diversity but faced challenges such as biased models or selecting insignificant samples. To address these issues

multilevel active learning (MAL) is introduced

which leverages deep textual information at both the sentence and word levels

taking into account the complex structure of the Chinese language. By integrating the sentence-level features extracted from bidirectional encoder representations from Transformers (BERT) embeddings and the word-level probability distributions obtained through a conditional random field (CRF) model

MAL comprehensively captures the Chinese textual affective structure (CTAS). Experimental results demonstrate that MAL significantly reduces annotation costs by approximately 70% and achieves more consistent performance compared to baseline methods.

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