Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
Refereed conference paper presented and published in conference proceedings

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其它資訊
摘要We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.
著者Xin LI, Wai LAM
會議名稱Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)
會議開始日07.09.2017
會議完結日11.09.2017
會議地點Copenhagen
會議國家/地區丹麥
會議論文集題名Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
出版年份2017
出版社Association for Computational Linguistics
頁次2886 - 2892
國際標準書號978-1-945626-83-8
語言美式英語

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