Aspect Term Extraction with History Attention and Selective Transformation
This work addresses a key sub-task in sentiment analysis for online reviews, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles Aspect Term Extraction by introducing a framework that uses opinion summaries and aspect detection history to improve predictions, achieving state-of-the-art results on four benchmark datasets.
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. Another clue is the information of aspect detection history, and it is distilled from the previous aspect predictions so as to 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.