Prior Knowledge Augmentation Network for Aspect-based Sentiment Analysis

Min Feng, Yanhua Yu, Kangkang Lu, Zirui Guo, Xiuxiu Cai, Jie Li

Abstract: Aspect-based sentiment analysis is a popular task in Natural Language Processing (NLP). Many methods utilize attention mechanisms and graph neural networks on dependency trees to identify the most relevant opinion words for aspects. However, improvements are limited due to the presence of rare words in small datasets and the inability to guarantee that each token will have a high probability of interacting with aspects. To address these challenges, we propose a Prior Knowledge Augmentation Network (PKAN) model that incorporates rich semantic information from knowledge graphs, syntactic structures, and prior probabilistic position embeddings. Specifically, to better comprehend semantic information, we design a knowledge augmentation module that constructs an adjacency matrix based on knowledge graphs, dependency trees and dataset co-occurrences. Additionally, to capture opinion words more comprehensively, we propose a probabilistic relative position embedding strategy. This strategy assumes that the semantic distribution of tokens at varying distances from the central word obeys a gaussian distribution and constrains the distance between words and aspects within one standard deviation. Experimental results on five public datasets validate the effectiveness of our model.