%0 Journal Article %A CAO Liuwen %A ZHOU Yanyan %A WU Changxing %A HUANG Zhaohua %T Mutual Learning Based Multiple Word Embeddings Fusion Framework for Sentiment Classification %D 2022 %R %J Journal of Chinese Information Processing %P 164-172 %V 36 %N 7 %X Aspect-level sentiment classification is a popular research topic with the purpose of automatically inferring the sentiment polarities of aspects in text. With the fusion of multiple word embeddings as input, models based on deep learning achieve promising performance on this task. Instead of concatenating different word embeddings, this paper proposes a multiple word embeddings fusing framework based on mutual learning, in which general word embeddings, domain-specific word embeddings and the sentiment word embeddings are combined to boost the performance. Specifically, we first construct the main model with the fusion of these three kinds of word embeddings as input, then build three auxiliary models with each single word embeddings as input, and finally jointly train the main model and three auxiliary models in a mutual learning manner. Experiments on three widespread datasets show that the performance of the proposed model is significantly better than those of benchmark methods. %U http://jcip.cipsc.org.cn/EN/abstract/article_3365.shtml