摘要
基于机器学习的文本分类方法通常忽略了文本上下文内容的语义特征,基于深度学习的文本分类方法虽考虑了上下文内容的语义特征,但弱化了实体信息在文本分类中的作用,无法丰富文本语义表示,突出文本内容特征.本文融合实体信息,提出一种循环神经网络与实体表示相结合的分类模型.该模型通过循环神经网络和Attention机制对文本进行建模表示,充分挖掘文本上下文内容的语义特征;同时,将实体表示与文本表示做注意力计算,进一步丰富文本语义与内容特征.在20NG、R8、IMDB、AG News四个数据集上进行实验,实验结果显示,该模型与其他主流基线方法相比,在准确率(Accuracy)上取得显著提高.
The text classification method based on machine learning usually ignores the semantic features of text context content.Although the text classification method based on deep learning takes the semantic features of text context content into account,it weakens the role of entity information in text classification and cannot enrich the text semantic representation and highlight the characteristics of text content.This paper proposes a classification model combining recurrent neural network and entity representation by fusing entity information.This model uses a recurrent neural network and attention mechanism to model text representations to mine semantic features of text context content.At the same time,the entity representation and text representation are used to perform attention calculations to further enrich text semantics and content features.Experiments on four data sets,such as 20 New groups,R8,IM DB,and AG New s,the experimental results show that the model has a significant improvement in accuracy compared with other mainstream baseline methods.
作者
杨春霞
吴佳君
李欣栩
YANG Chun-xia;WU Jia-jun;LI Xin-xu(Nanjing University of Information Science&Technology,Automation Institute,Nanjing 210044,China;Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT),Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing 210044,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2020年第12期2516-2521,共6页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(51705260,61273229)资助
南京信息工程大学国家社科重大项目培育项目资助。
关键词
文本分类
实体信息
循环神经网络
注意力机制
text classification
entity information
recurrent neural network
attention mechanism