Motive imagery scoring based on deep neural network
The purpose of this study is to develop a method for scoring the motive imageries in text materials. According to Winter’s motive scoring system, there are three different kinds of motive imageries and each of them is given detailed definitions and scoring rules. But it’s difficult and also time-con...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78596 |
| _version_ | 1826120408524390400 |
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| author | Yu, Sicheng |
| author2 | Chen Lihui |
| author_facet | Chen Lihui Yu, Sicheng |
| author_sort | Yu, Sicheng |
| collection | NTU |
| description | The purpose of this study is to develop a method for scoring the motive imageries in text materials. According to Winter’s motive scoring system, there are three different kinds of motive imageries and each of them is given detailed definitions and scoring rules. But it’s difficult and also time-consuming to implement these rules manually. The traditional machine learning methods also have difficulties in extracting features. With the evolution and development of deep learning, deep neural networks have played an important role in data processing. In the study, three different deep learning models, including TextCNN, LSTM and Bidirectional LSTM with attention mechanism, are applied to score the motives. The performances of three models are evaluated, compared, and reported in this thesis. |
| first_indexed | 2024-10-01T05:15:58Z |
| format | Thesis |
| id | ntu-10356/78596 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:15:58Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/785962023-07-04T16:19:20Z Motive imagery scoring based on deep neural network Yu, Sicheng Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The purpose of this study is to develop a method for scoring the motive imageries in text materials. According to Winter’s motive scoring system, there are three different kinds of motive imageries and each of them is given detailed definitions and scoring rules. But it’s difficult and also time-consuming to implement these rules manually. The traditional machine learning methods also have difficulties in extracting features. With the evolution and development of deep learning, deep neural networks have played an important role in data processing. In the study, three different deep learning models, including TextCNN, LSTM and Bidirectional LSTM with attention mechanism, are applied to score the motives. The performances of three models are evaluated, compared, and reported in this thesis. Master of Science (Signal Processing) 2019-06-24T05:38:59Z 2019-06-24T05:38:59Z 2019 Thesis http://hdl.handle.net/10356/78596 en 69 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Yu, Sicheng Motive imagery scoring based on deep neural network |
| title | Motive imagery scoring based on deep neural network |
| title_full | Motive imagery scoring based on deep neural network |
| title_fullStr | Motive imagery scoring based on deep neural network |
| title_full_unstemmed | Motive imagery scoring based on deep neural network |
| title_short | Motive imagery scoring based on deep neural network |
| title_sort | motive imagery scoring based on deep neural network |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | http://hdl.handle.net/10356/78596 |
| work_keys_str_mv | AT yusicheng motiveimageryscoringbasedondeepneuralnetwork |