Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series; 28th International C Igor V. Tetko,Věra K?rková,Fabian

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Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Atten Secondly, our model uses neural collaborative filtering to capture the implicit interaction influences between user and product. Lastly, our model makes full use of both explicit and implicit informations for final classification. Experimental results show that our model outperforms state-of-the-ar

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Revising Attention with Position for Aspect-Level Sentiment Classificationition information and attention mechanism. We get the position distribution according to the distances between context words and target, then leverage the position distribution to modify the attention weight distribution. In addition, considering that sentiment polarity is usually represented by a p

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Springer Series in Materials Science a Chinese machine reading comprehension competition, namely the LES Cup Challenge, in October 2018. The competition introduces a big dataset of long articles and improperly labelled data, therefore challenges the state-of-the-art methods in this area. We proposed an ensemble model of four novel rec

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Domain Walls in Ferroelectric Materials,However, directly applying the MT model to CWS task would introduce translation errors and result in poor word segmentation. In this paper, we propose a novel method named Translation Correcting to solve this problem. Based on the differences between CWS and MT, Translation Correcting eliminates tra