基于对抗网络的文本对齐跨语言情感分类方法  

A Text-Aligned Cross-Language Sentiment Classification Method Based on Adversarial Networks

作　　者：杨文丽 李娜娜 Yang Wenli;Li Nana(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401

出　　处：《数据分析与知识发现》2022年第7期141-151,共11页Data Analysis and Knowledge Discovery

基　　金：国家自然科学青年基金项目(项目编号:61806072)的研究成果之一。

摘　　要：【目的】通过拉近共享空间中双语文本对的分布以提高跨语言情感分类的准确率。【方法】在情感知识迁移过程中,通过调整平衡系数同时对齐词对和文本对,并联合语言判别器生成对抗网络优化转换矩阵。此外,采用一种多特征融合的分层神经网络方法表示文本,同时兼顾单词和句子的上下文主题关联性,能够有效解决文本长距离特征依赖问题。【结果】在NLP&CC 2013标准数据集上的实验结果证明,所提方法的平均跨语言情感分类准确率达到83.66%,比基准模型平均提高2.30个百分点。【局限】只在中英文数据集上进行实验,在其他语言组合中的有效性需要进一步验证。【结论】通过提高双语文本相似度的方式能够有效提高跨语言情感分类的准确率。[Objective] The paper tries to improve the accuracy of cross-language sentiment classification by narrowing the distribution of bilingual text pairs in the shared space. [Methods] In the process of emotional knowledge transfer, we aligned the word and text pairs simultaneously by adjusting the balance coefficient. Then,we combined the language discriminator to generate the conversion matrix for adversarial network optimization.Finally, we used a multi-feature fusion hierarchical neural network to represent the texts, the contexts, as well as the topic relevance of words and sentences, which addressed the issue of long-distance feature dependence of the texts. [Results] We examined our model on the NLP&CC 2013 standard data sets and the average cross-language sentiment classification accuracy was 83.66%, which was 2.30% higher than the benchmark model. [Limitations]This method was only tested with Chinese and English datasets. More research is needed to evaluate its effectiveness with other languages. [Conclusions] Improving the similarity of bilingual texts could effectively increase the accuracy of cross-language sentiment classification.

关 键 词：词对齐 文本对齐 生成对抗网络 多特征融合 分层神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]