A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11344 |
| _version_ | 1827675086847475712 |
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| author | Wei Ke Ka-Hou Chan |
| author_facet | Wei Ke Ka-Hou Chan |
| author_sort | Wei Ke |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:57:14Z |
| format | Article |
| id | doaj.art-697d51d33e834bbc9cd960939e2c2cec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:57:14Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-697d51d33e834bbc9cd960939e2c2cec2023-11-23T02:06:37ZengMDPI AGApplied Sciences2076-34172021-11-0111231134410.3390/app112311344A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment AnalysisWei Ke0Ka-Hou Chan1School of Applied Sciences, Macao Polytechnic Institute, Macao, ChinaSchool of Applied Sciences, Macao Polytechnic Institute, Macao, ChinaParagraph-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.https://www.mdpi.com/2076-3417/11/23/11344NLPsentiment analysisChebyshev poolingmultilayerCARU |
| spellingShingle | Wei Ke Ka-Hou Chan A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis Applied Sciences NLP sentiment analysis Chebyshev pooling multilayer CARU |
| title | A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis |
| title_full | A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis |
| title_fullStr | A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis |
| title_full_unstemmed | A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis |
| title_short | A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis |
| title_sort | multilayer caru framework to obtain probability distribution for paragraph based sentiment analysis |
| topic | NLP sentiment analysis Chebyshev pooling multilayer CARU |
| url | https://www.mdpi.com/2076-3417/11/23/11344 |
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