基于对抗双向GRU网络的跨语言情感分类方法
CROSS-LANGUAGE SENTIMENT CLASSIFICATION METHOD BASED ON ADVERSARIAL BIDIRECTIONAL GATE RECURRENT UNIT NETWORK
李雪芹 1杨文丽 1李娜娜2
作者信息
- 1. 河北工业大学人工智能与数据科学学院 天津 300401
- 2. 河北工业大学人工智能与数据科学学院 天津 300401;河北工业大学河北省大数据重点实验室 天津 300401
- 折叠
摘要
为了提高资源匮乏语言的情感分类性能,提出一种基于对抗双向GRU网络相结合的跨语言情感分类模型(ABi-GRU).通过基于语义双语词嵌入方法来提取中英文文本词向量特征;结合注意力机制的双向GRU网络提取文本的上下文情感特征,同时引入生成对抗网络缩小中英文向量特征分布之间的差距;通过情感分类器进行情感分类.实验结果分析表明,该方法有效地提升了跨语言情感分类的准确率.
Abstract
In order to improve sentiment classification performance of resource-scarce languages,a cross-lingual sentiment analysis classification model(ABi-GRU)based on the combination of adversarial bidirectional GRU network is proposed.The model extracted the word vector features of Chinese and English texts based on semantic bilingual word embedding.Combining with the bidirectional GRU network of attention mechanism,the text's contextual emotion features were extracted and the generative adversarial network was introduced to narrow the gap between Chinese and English vector feature distribution.The sentiment classification was carried out by sentiment classifier.Experimental results show that this method effectively improves the accuracy of cross-language sentiment classification.
关键词
跨语言情感分类/注意力机制/生成对抗网络/双向GRU网络Key words
Cross-language sentiment classification/Attention mechanism/Generative adversarial network/Bidirec-tional gate recurrent unit network(Bi-GRU)引用本文复制引用
基金项目
国家自然科学基金青年科学基金项目(61806072)
天津市应用基础与前沿技术研究计划基金项目(16JCYBJC15600)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据