Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022)

Abstract: Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews, which is of great value for evaluating the fine-grained performance of a product. The most recent solutions tackle this problem via weakly supervised methods, achieving remarkable improvement over unsupervised methods. However, a closer look at these methods reveals that the required human efforts are nontrivial and can sometimes be hard to obtain. In this study, we explore the possibility of minimizing human guidance while improving detection performance, with a deep clustering method that relies merely on the category name of each aspect and a pretrained language model (LM). The LM, combined with prompt techniques, is employed as a knowledge base to automatically generate constraints for clustering, as well as to provide a representation space to perform the clustering. Our method (1) extracts extensive keywords to expand our understanding of each aspect, (2) automatically generates instance-level and concept-level constraints for clustering, and (3) trains the clustering model with the above constraints. We demonstrate the capability of the proposed framework through extensive experiments on nine benchmark datasets. Our model not only performs noticeably better than existing unsupervised approaches but also considerably surpasses weakly supervised methods that require more human efforts.

## methodology figure 2 shows our clustering framework for aspect detection, which utilizes only the label names of aspect categories and a pre-trained lm h. the key to high performance is to effectively induce aspectdetection-specific knowledge from h. our framework contains three steps: (1) an expansive understanding of the aspect categories; (2) the generation of two task-specific cluster constraints based on aspect understanding; (3) a constrained clustering training method over the prompt-induced representation space. we use bert for lm in this study, but our framework can be compatible with more powerful pre-trained lms. ## conclusion in this paper, we propose a prompt-based constrained clustering method for aspect category detection. first, we automatically extract a comprehensive set of keywords for each aspect. based on the keywords, we construct instance-and conceptlevel clustering constraints. finally, a joint training objective is proposed for optimal aspect detection performance. experiments show that our method not only learns high-quality aspect keywords, but also significantly outperforms both unsupervised and weakly supervised methods. in the future, we plan to extend our framework to jointly perform aspect and sentiment detection for absa.