High-Order Pair-Wise Aspect and Opinion Terms Extraction With Edge-Enhanced Syntactic Graph Convolution
Shengqiong Wu, Hao Fei, Yafeng Ren, Bobo Li, Fei Li, Donghong Ji
Abstract
Pair-wise aspect and opinion terms extraction (PAOTE), aiming at detecting the pair of the correlated aspect and opinion terms jointly, recently has drawn increasing research attention in the community of sentiment analysis and opinion mining. Recent works largely employ joint methods for the task, while they do not sufficiently incorporate the external syntactic knowledge, such as the dependency edges and labels. Besides, these methods fail to capture the underlying shared interactions among the overlapping aspect-opinion pair structures. In this paper we address the above issues for better PAOTE. Specifically, we present a span graph-based model for joint PAOTE, based on which we propose a edge-enhanced syntactic graph convolutional network (ESGCN) to simultaneously encode the syntactic dependency edges and the corresponding labels, for enhancing the extraction and pairing of aspect and opinion terms. During relational pairing, we employ a Biaffine and a Triaffine scorer respectively for high-order scoring, fully exploring the mutual interactions among different aspect-opinion pairs. Experimental results on four benchmark datasets show that our proposed method significantly outperforms all the baseline systems, giving the state-of-the-art performances. Further analysis proves the effectiveness of our framework on modeling the syntactic edge and label knowledge, as well as the advance of the high-order pairing mechanism for the task.