A Knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Misc</i> aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.