摘要
针对文本情感分类任务中,有情感标注的语料在不同语言中的不均衡问题,结合深度学习和迁移学习,提出一种基于对抗长短时记忆网络(ALSTM)的跨语言文本情感分类方法.设置双语各自独立的特征提取网络和共享特征提取网络,把获取到的特征拼接输入到分类器进行分类.在共享特征提取网络中,设置语言分类器,运用对抗思想优化模型,通过投票法决定文本最终的情感极性.实验表明:该方法可以取得跨语言文本情感分类任务更高的准确度.
This paper proposes a cross-lingual sentiment classification method based on adversarial long short term memory(ALSTM)network,which aims at the problem of text sentiment classification in the disparity of emotionally annotated corpus in different languages,combined with deep learning and transfer learning.Bilingual feature extraction networks and a shared feature extraction network are set up,and then the extracted features are merged for classification.In the shared feature extraction network,a language classifier is set up.Using the adversarial idea to optimize the model,and the final polarity of the text depends on the voting results.Experiments show that cross-lingual sentiment classification can achieve higher accuracy by this method.
作者
党莉
陈锻生
张洪博
DANG Li;CHEN Duansheng;ZHANG Hongbo(College of Computer Science and Technology,Huaqiao University,Xiamen 361021,China)
出处
《华侨大学学报（自然科学版）》
CAS
北大核心
2019年第2期251-256,共6页
Journal of Huaqiao University(Natural Science)
基金
国家自然科学基金资助项目(61502182)
福建省科技计划重点项目(2015H0025)
关键词
文本情感
跨语言
对抗
长短时记忆网络
共享特征
sentiment of the text
cross-lingual
adversarial
long short term memory network
shared features
