Deep learning metaphor detection with emotion-cognition association
The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutiona...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/44252/ |
| _version_ | 1848850425978028032 |
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| author | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala |
| author_facet | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala |
| author_sort | Razali, Md Saifullah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process. |
| first_indexed | 2025-11-15T10:06:06Z |
| format | Conference or Workshop Item |
| id | upm-44252 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T10:06:06Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-442522023-11-15T08:19:25Z http://psasir.upm.edu.my/id/eprint/44252/ Deep learning metaphor detection with emotion-cognition association Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process. IEEE 2022 Conference or Workshop Item PeerReviewed Razali, Md Saifullah and Abdul Halin, Alfian and Chow, Yang-Wai and Mohd Norowi, Noris and Doraisamy, Shyamala (2022) Deep learning metaphor detection with emotion-cognition association. In: 2022 International Conference on Digital Transformation and Intelligence (ICDI), 1-2 Dec. 2022, Borneo Conventional Centre Kuching, Sarawak, Malaysia. (pp. 8-14). https://ieeexplore.ieee.org/document/10007398 10.1109/ICDI57181.2022.10007398 |
| spellingShingle | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala Deep learning metaphor detection with emotion-cognition association |
| title | Deep learning metaphor detection with emotion-cognition association |
| title_full | Deep learning metaphor detection with emotion-cognition association |
| title_fullStr | Deep learning metaphor detection with emotion-cognition association |
| title_full_unstemmed | Deep learning metaphor detection with emotion-cognition association |
| title_short | Deep learning metaphor detection with emotion-cognition association |
| title_sort | deep learning metaphor detection with emotion-cognition association |
| url | http://psasir.upm.edu.my/id/eprint/44252/ http://psasir.upm.edu.my/id/eprint/44252/ http://psasir.upm.edu.my/id/eprint/44252/ |