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.

翻译：方面类别检测（ACD）旨在识别给定评论语句中的隐式和显式方面。当前最先进的ACD方法采用深度神经网络（DNN）将该问题视为多标签分类任务。然而，类别特定表示的学习高度依赖于标注样本的数量，这在现实场景中往往难以获得。本文提出一种新方法，通过在有监督设置下将深度神经网络与渐进式机器学习（GML）相结合来解决ACD任务。我们旨在利用DNN在语义关系建模方面的优势，在GML渐进推理过程中促进标注实例与非标注实例间的有效知识迁移。为实现这一目标，我们首先分析DNN学习的隐空间以建模实例间的关系（即相似或对立关系），然后将这些关系表示为因子图中的二值特征以高效传递知识。最后，我们在真实基准数据集上对所提方案进行对比研究，结果表明与DNN协作进行特征提取的GML方法始终优于纯DNN解决方案。