Abstract
Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.
Highlights
Multilayer Content-Adaptive Recurrent Unit (CARU) Framework to Sentiments can be reflected as the voluntary or involuntary reactions of human beings to the outside world when they conduct behaviors, such as talking, thinking, communicating, learning, and decision making
We extended our previous work [8] to implement CARU in a multilayer architecture, where the output, i.e., a hidden state, of each CARU cell was connected to the input of the upper cell in a higher level
Well-designed feature extraction is crucial for the Natural language processing (NLP) tasks, having to be able to capture the subtle differences between the sentences in a paragraph
Summary
Multilayer CARU Framework to Sentiments can be reflected as the voluntary or involuntary reactions of human beings to the outside world when they conduct behaviors, such as talking, thinking, communicating, learning, and decision making. Since all these and similar behaviors can be expressed in sentences and paragraphs, sentiment analysis tasks have great significance to our daily life. There are often situations impacted by the current emotions, and the use of words and structures of the presented sentence change This kind of sequences makes the analysis process more difficult and time-consuming.
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