Investigating the use of deep learning for textual entailment in BRACU-NLI dataset
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
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| Jazyk: | English |
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Brac University
2024
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| On-line přístup: | http://hdl.handle.net/10361/22858 |
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10361-228582024-05-19T21:04:01Z Investigating the use of deep learning for textual entailment in BRACU-NLI dataset Haque, Farah Binta Yasin, MD Saha, Shishir Hossain, MD Mazed Sadeque, Farig Yousuf Department of Computer Science and Engineering, Brac University Deep learning Machine learning Text entailment Text summarizing Text generation Neural networks (Computer science) Natural language processing (Computer science) Computational linguistics This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 50-51). This work aims to analyze the potential of deep neural models for text-based entailment in Bangla Language. Entailment is the method of determining whether one text infers or goes against another text. The study concentrates on the application of deep learning methods, such as Recurrent Neural Networks (RNNs), BERT, GPT for solving text-based entailment. The neural network method is trained to foretell the relationship between two text sequences, such as whether one text sequence entails the other or whether one text sequence provides evidence for the other. Other tasks, such as question answering, can also be tackled by fine-tuning these models on specific datasets. The findings of this work will contribute to the development of further developed NLP systems that can perform complex reasoning and entailment tasks. Farah Binta Haque MD Yasin Shishir Saha MD Mazed Hossain B.Sc in Computer Science and Engineering 2024-05-19T03:32:05Z 2024-05-19T03:32:05Z ©2024 2024-01 Thesis ID: 24141090 ID: 20301310 ID: 20301320 ID: 21301569 http://hdl.handle.net/10361/22858 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 60 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep learning Machine learning Text entailment Text summarizing Text generation Neural networks (Computer science) Natural language processing (Computer science) Computational linguistics |
| spellingShingle |
Deep learning Machine learning Text entailment Text summarizing Text generation Neural networks (Computer science) Natural language processing (Computer science) Computational linguistics Haque, Farah Binta Yasin, MD Saha, Shishir Hossain, MD Mazed Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. |
| author2 |
Sadeque, Farig Yousuf |
| author_facet |
Sadeque, Farig Yousuf Haque, Farah Binta Yasin, MD Saha, Shishir Hossain, MD Mazed |
| format |
Thesis |
| author |
Haque, Farah Binta Yasin, MD Saha, Shishir Hossain, MD Mazed |
| author_sort |
Haque, Farah Binta |
| title |
Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| title_short |
Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| title_full |
Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| title_fullStr |
Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| title_full_unstemmed |
Investigating the use of deep learning for textual entailment in BRACU-NLI dataset |
| title_sort |
investigating the use of deep learning for textual entailment in bracu-nli dataset |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22858 |
| work_keys_str_mv |
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| _version_ |
1814309073562107904 |