Deep learning for sentence/text classification
Deep Learning Architectures have been achieving state-of-the-art results in many application scenarios. Particularly, the performance of Deep Convolution Neural Networks (Deep ConvNets) in computer vision tasks is incontestable. The wave of ConvNets is sweeping through other applications other than...
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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/76043 |
| _version_ | 1826116314620493824 |
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| author | Yu, Rongqian |
| author2 | Ponnuthurai N. Suganthan |
| author_facet | Ponnuthurai N. Suganthan Yu, Rongqian |
| author_sort | Yu, Rongqian |
| collection | NTU |
| description | Deep Learning Architectures have been achieving state-of-the-art results in many application scenarios. Particularly, the performance of Deep Convolution Neural Networks (Deep ConvNets) in computer vision tasks is incontestable. The wave of ConvNets is sweeping through other applications other than vision tasks. There are some instances of ConvNets used for Natural Language Processing (NLP) tasks such as sentence/text classification. The objective of this project is applying Deep Learning models such as Recurrent Neural Networks, ConvNets for sentence/text classification tasks and suggest ways to improve their performance. In this design, I used CNN(Convolution neural network) network structure as my framework, using python3 programming language and PyTorch deep learning tool to complete the preparation of the software and experiments on the remote server in the laboratory to get the final result(using GPU acceleration). |
| first_indexed | 2024-10-01T04:09:18Z |
| format | Thesis |
| id | ntu-10356/76043 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:09:18Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/760432023-07-04T16:40:08Z Deep learning for sentence/text classification Yu, Rongqian Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Deep Learning Architectures have been achieving state-of-the-art results in many application scenarios. Particularly, the performance of Deep Convolution Neural Networks (Deep ConvNets) in computer vision tasks is incontestable. The wave of ConvNets is sweeping through other applications other than vision tasks. There are some instances of ConvNets used for Natural Language Processing (NLP) tasks such as sentence/text classification. The objective of this project is applying Deep Learning models such as Recurrent Neural Networks, ConvNets for sentence/text classification tasks and suggest ways to improve their performance. In this design, I used CNN(Convolution neural network) network structure as my framework, using python3 programming language and PyTorch deep learning tool to complete the preparation of the software and experiments on the remote server in the laboratory to get the final result(using GPU acceleration). Master of Science (Computer Control and Automation) 2018-09-24T07:57:01Z 2018-09-24T07:57:01Z 2018 Thesis http://hdl.handle.net/10356/76043 en 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yu, Rongqian Deep learning for sentence/text classification |
| title | Deep learning for sentence/text classification |
| title_full | Deep learning for sentence/text classification |
| title_fullStr | Deep learning for sentence/text classification |
| title_full_unstemmed | Deep learning for sentence/text classification |
| title_short | Deep learning for sentence/text classification |
| title_sort | deep learning for sentence text classification |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/76043 |
| work_keys_str_mv | AT yurongqian deeplearningforsentencetextclassification |