Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction
Cong Feng, Yuan Rao, Ambreen Nazir, Lianwei Wu, Long He · Procedia Computer Science · 2020
With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance.