基于LAAE网络的跨语言短文本情感分析方法

跨语言短文本情感分析作为自然语言处理领域的一项重要的任务, 近年来备受关注. 跨语言情感分析能够利用资源丰富的源语言标注数据对资源匮乏的目标语言数据进行情感分析, 建立语言之间的联系是该任务的核心.与传统的机器翻译建立联系方法相比, 迁移学习更胜一筹, 而高质量的跨语言文本向量则会提升迁移效果. 本文提出LAAE网络模型, 该模型通过长短记忆网络(LSTM)和对抗式自编码器(AAE)获得含上下文情感信息的跨语言向量, 然后利用双向GRU (Gated Recurrent Unite)进行后续情感分类任务. 其中, 分类器首先在源语言上进行训练,最后迁移到目标语言上进行分类任务. 本方法的有效性体现在实验结果中.

Cross-Lingual Short Text Sentiment Analysis via LAAE

As a significant task in natural language processing, cross-lingual sentiment analysis is able to leverage the data and models available in rich-resource languages when solving any problem in scarce-resource settings, which has acquired widespread attention. Its core is to establish the connection between languages. In this respect, transfer learning performs better than traditional translation methods and can be enhanced by high-quality cross-lingual text vectors. Therefore, we propose an LAAE model in this study, which uses Long Short Term Memory (LSTM) and an Adversarial AutoEncoder (AAE) to generate contextual cross-lingual vectors and then applies the Bidirectional Gated Recurrent Unit (BiGRU) for subsequent sentiment classification. Specifically, the training in the source language is transferred to that in the target language for classification. The results prove that the proposed method is effective.