【摘要】 方面级情感分类是当前的研究热点之一，其目标是自动推断文本中特定方面的情感倾向。融合多种不同类型的词向量作为基于深度学习模型的输入，在该任务上取得了较好的效果。然而，通过直接拼接或门控机制等方式融合多种不同的词向量，不能充分发挥每种词向量的作用。为了解决这个问题，该文提出了一种基于互学习的多词向量融合情感分类框架，其目的是充分利用普通词向量、领域词向量和情感词向量中的信息，提高分类的性能。具体地，首先构建以三种词向量的融合作为输入的主模型，然后分别构建三个以单一词向量作为输入的辅助模型，最后基于互学习的方式联合训练主模型和辅助模型，以达到相互促进的效果。在三个常用数据集上的实验表明，该文提出框架的性能明显好于基准方法。更多还原

【Abstract】 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.更多还原