학술논문
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
Document Type
Academic Journal
Source
计算机应用与软件 / Computer Applications and Software. 41(1):82-88
Subject
Language
Chinese
ISSN
1000-386X
Abstract
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.
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.
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.