  Aspect Category Detection (ACD) aims to identify implicit and explicit
aspects in a given review sentence. The state-of-the-art approaches for ACD use
Deep Neural Networks (DNNs) to address the problem as a multi-label
classification task. However, learning category-specific representations
heavily rely on the amount of labeled examples, which may not readily available
in real-world scenarios. In this paper, we propose a novel approach to tackle
the ACD task by combining DNNs with Gradual Machine Learning (GML) in a
supervised setting. we aim to leverage the strength of DNN in semantic relation
modeling, which can facilitate effective knowledge transfer between labeled and
unlabeled instances during the gradual inference of GML. To achieve this, we
first analyze the learned latent space of the DNN to model the relations, i.e.,
similar or opposite, between instances. We then represent these relations as
binary features in a factor graph to efficiently convey knowledge. Finally, we
conduct a comparative study of our proposed solution on real benchmark datasets
and demonstrate that the GML approach, in collaboration with DNNs for feature
extraction, consistently outperforms pure DNN solutions.
