摘要
对微博多分句的评论,ELMo-Text CNN、GPT等模型不能准确提取文本上下文联系,导致分类效果不理想。为了解决此问题,采用BERT-Text CNN模型,利用BERT独特自注意力机制的双向编码转换器结构获得具有句子全局特征的字向量,将字向量输入到Text CNN中,利用Text CNN捕获局部特征的能力,最终提取语义、语序以及上下文联系等高阶特征,解决了模型不能准确获取文本上下文联系的问题,实现了高准确率的微博评论细粒度情感分类。同时为验证该模型的优势,与现有模型进行比较,在simplifyweibo_4_moods数据集上测试结果显示BERT-Text CNN模型在准确率、召回率以及F1指标方面均有提升。
For comments with multiple sections within sentences,some state-of-art models,such as Embedding from Language Models-Text Convolutional Neural Network and Generative Pre-trained Transformer model,cannot accurately extract the meaning and therefore result in unsatisfactory performance.To solve this problem,we utilize Bidirectional Encoder Representations from Transformers-Text Convolutional Neural Network and Generative Pre-trained Transformer model.Using the bidirectional code converter structure of BERT′s unique self-attention mechanism,we can obtain the word vector of the global feature of the sentence,then we input the word vectors into Text CNN,then using Text CNN to capture local features,finally we extract high-level features,such as semantics and contextual connection.This process solved the problem of inaccurate contextual connection of the text obtained by the model,allowing us to realize the fine-grained sentiment classification of Weibo comments with high accuracy.Meanwhile,to verify the advantages of the model,we compared it with existing models.The test results on the simplifyweibo_4_moods dataset show that the BERT-Text CNN model has improved accuracy,recall,and F1 indicators.
作者
徐凯旋
李宪
潘亚磊
XU Kaixuan;LI Xian;PAN Yalei(Institute of Complexity Science,Qingdao University,Qingdao 266071,China;Institude For Future,Qingdao University,Qingdao 266071,China)
出处
《复杂系统与复杂性科学》
CAS
CSCD
北大核心
2021年第2期89-94,共6页
Complex Systems and Complexity Science
