DRGCN Multitasking for Aspect-Based Sentiment Analysis

Existing aspect-based sentiment analysis (ABSA) methods do not sufficiently enhance multiple subtasks with syntactic knowledge in a joint framework. In this paper, we propose an ABSA method that utilizes a multitask learning framework to enhance syntactic knowledge fully. The method first builds on a dependency relation embedded graph convolutional network to learn syntactic dependencies and the dependency types between words in a sentence fully. Second, to make better use of the syntactic information between aspect and opinion words, we extend the adjacency matrix based on dependency parsing to establish the direct relationship between aspect and opinion words. Finally, an information passing mechanism is exploited to ensure that our model learns from multiple related tasks in a multitask learning framework. The results of experiments on three public datasets, namely LAP14, REST14, and REST15, show that the proposed method has better performance than the baseline method.