Aspect Term Extraction with History Attention and Selective Transformation

Aspect Term Extraction with History Attention and Selective Transformation

Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4194-4200. https://doi.org/10.24963/ijcai.2018/583

Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews. We present a new framework for tackling ATE. It can exploit two useful clues, namely opinion summary and aspect detection history. Opinion summary is distilled from the whole input sentence, conditioned on each current token for aspect prediction, and thus the tailor-made summary can help aspect prediction on this token. On the other hand, the aspect detection history information is distilled from the previous aspect predictions, and it can leverage the coordinate structure and tagging schema constraints to upgrade the aspect prediction. Experimental results over four benchmark datasets clearly demonstrate that our framework can outperform all state-of-the-art methods. 
Keywords:
Natural Language Processing: Natural Language Processing
Natural Language Processing: Sentiment Analysis and Text Mining