Open AccessA Small amount of Labeled Data Chinese Online Course Review Target Extraction via ALBERT-IDCNN-CRF Model
Author(s) -
Min Liang,
Xianglin Miao,
Peng Bi,
Feijuan He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012049
Subject(s) - computer science , sentence , conditional random field , annotation , artificial intelligence , coding (social sciences) , natural language processing , data extraction , convolutional neural network , transformer , crfs , machine learning , statistics , physics , mathematics , medline , quantum mechanics , voltage , political science , law
Aspect sentiment analysis of online course reviews is of great significance in helping users choose courses and improve course quality. Review target extraction is particularly important as the basis of aspect sentiment analysis. Because the current models mostly rely on a large amount of annotation data, there is fewer relevant research on the extraction of online course review targets with higher annotation costs. This paper proposes an ALBERT-IDCNN-CRF review target extraction model for a small amount of labeled data. First, using ALBERT pre-trained sentences obtained dynamic model Chinese word vector coding; Simultaneously, using ALBERT pre-trained model of Transformer obtain sentence abstract features. Then, abstract features are input into the dilated convolutional neural network (IDCNN) to reduce the number of neuron layers and parameters. Finally, conditional random field (CRF) is used to decode and annotate the review sentences to extract the appropriate review objectives. The experimental results on the school online real Chinese online course review data set show that our model has achieved better results than existing models.