Extracting Aspect Words from Online Course Reviews Based on MacBERT and Adversarial Training
The current online education datasets are relatively small,and annotated data for aspect term extraction tasks is rela-tively scarce.To address this issue,a proposal is made to use online course reviews to create corresponding datasets.In order to vali-date the effectiveness of aspect term extraction methods based on deep learning,a model for aspect term extraction based on MacBERT and adversarial training is proposed.This model utilizes the MacBERT layer to extract semantic information from the text and convert it into word vectors.It adds a certain amount of perturbation to the original word vectors to generate adversarial sam-ples,thereby improving the model's robustness.Subsequently,the model further obtains contextual information through the BiL-STM layer,and finally uses CRF to optimize the model results and obtain the best predicted sequence.Experimental results demon-strate that in the constructed online course review dataset and the People's Daily public dataset,the model's identification results outperform other mainstream neural network-based aspect term extraction models,with improvements of 7.45%and 7.06%over the BERT-BiLSTM-CRF model,respectively,proving the feasibility of this approach.
aspect word extractionMacBERTadversarial trainingBiLSTMCRF
万方数据
维普
