Transformation Networks for Target-Oriented Sentiment Classification
Refereed conference paper presented and published in conference proceedings


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摘要Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer. Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer. Experiments show that our model achieves a new state-of-the-art performance on a few benchmarks.
出版社接受日期15.07.2018
著者Xin Li, Lidong Bing, Wai Lam, Bei Shi
會議名稱The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)
會議開始日15.07.2018
會議完結日20.07.2018
會議地點Melbourne, Australia
會議國家/地區澳大利亞
會議論文集題名Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics
出版年份2018
月份7
頁次946 - 956
國際標準書號978-1-948087-32-2
語言英式英語

上次更新時間 2021-21-01 於 02:19