Aspect Term Extraction with History Attention and Selective Transformation
Refereed conference paper presented and published in conference proceedings

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AbstractAspect 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.1
Acceptance Date13/07/2018
All Author(s) ListXin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang
Name of Conference27th International Joint Conference on Artificial Intelligence (IJCAI 18)
Start Date of Conference13/07/2018
End Date of Conference19/07/2018
Place of ConferenceStockholm
Country/Region of ConferenceSweden
Proceedings TitleProceedings of the 27th International Joint Conference on Artificial Intelligence
Pages4194 - 4200
LanguagesEnglish-United Kingdom

Last updated on 2020-28-05 at 02:11