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


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摘要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.1
出版社接受日期13.07.2018
著者Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang
會議名稱27th International Joint Conference on Artificial Intelligence (IJCAI 18)
會議開始日13.07.2018
會議完結日19.07.2018
會議地點Stockholm
會議國家/地區瑞典
會議論文集題名Proceedings of the 27th International Joint Conference on Artificial Intelligence
出版年份2018
月份7
頁次4194 - 4200
國際標準書號978-099924112-7
國際標準期刊號10450823
語言英式英語

上次更新時間 2020-03-09 於 04:56