WEKO3
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Demonstration of Efficacy of Exploiting ChatGPT Data to the Transformers-Based Models by Performing Bangla Intent Analysis
http://hdl.handle.net/10228/0002001863
http://hdl.handle.net/10228/0002001863b4cac334-1a0e-4936-9b96-272220a547f8
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10462355.pdf (1.3 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-08-07 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Demonstration of Efficacy of Exploiting ChatGPT Data to the Transformers-Based Models by Performing Bangla Intent Analysis | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Al-Mahmud,
× Al-Mahmud,
× 嶋田, 和孝
WEKO
13734
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| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-sa/4.0/ | |||||||||||
| 権利情報 | Copyright (c) 2024 International Journal of Integrated Engineering. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. | |||||||||||
| 言語 | en | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | With the expanding mode of online opinion sharing, an automatic approach to intent analysis is necessary and useful in the practical scenario. Intent analysis inspects persons' and entities’ viewpoints from online user-created texts. Conventional sentiment analysis deals with two classes: positive and negative. In this study, to extend the conventional sentiment analysis task, intent analysis deals with more important classes to obtain deeper insights. Accordingly, this study deals with five classes: pessimism, optimism, suggestion, sarcastic, and miscellaneous. Intent analysis with machine learning essentially needs a massive amount of data to generate a robust model. However, manually accumulating the training data is expensive, particularly in less dominant languages like Bangla. Hence, to obtain sufficient training data, this study generates, collects, and pre-processs Bangla restaurant data for the task by OpenAI ChatGPT API through prompt and data augmentation. These data are called “source data”. As no user-generated Bangla data is available in the literature, this study prepares and validates a new Bangla intent analysis dataset by collecting user-generated real data. These data are referred to as “target data”. Source data is utilized to assist the target task (i.e., main task) performed on the target data. By utilizing both source and target data, three approaches are proposed: combined data approach, semi-supervised learning, and stepwise learning. Experimental results demonstrated that the proposed semi-supervised learning with transformers-based models is effective in improving the performance of the target data by exploiting ChatGPT-generated source data. The best F1 score of the proposed semi-supervised learning is 0.74, while that of the baseline is 0.72. Additionally, we proposed some feature concatenation methods. In this case, the highest F1 score is 0.75 | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : International Journal of Integrated Engineering 巻 16, 号 7, p. 12-25, 発行日 2024-11-27 |
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| 出版社 | ||||||||||||
| 出版者 | Penerbit UTHM | |||||||||||
| 言語 | en | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Intent analysis | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | conventional machine-learning models | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | transformers-based models | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | combined data technique | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | semi-supervised learning approach | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Stepwise learning approach | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.30880/ijie.2024.16.07.002 | |||||||||||
| URI | ||||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/18533 | |||||||||||
| 助成情報 | ||||||||||||
| 助成機関識別子タイプ | Crossref Funder | |||||||||||
| 助成機関識別子 | https://doi.org/10.13039/501100002241 | |||||||||||
| 助成機関名 | 科学技術振興機構 (JST) | |||||||||||
| 言語 | ja | |||||||||||
| 助成機関名 | Japan Science and Technology Agency (JST) | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ja | |||||||||||
| プログラム情報 | JST次世代研究者挑戦的研究プログラム | |||||||||||
| 研究課題番号タイプ | JGN | |||||||||||
| 研究課題番号 | JPMJSP2154 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 2229-838X | |||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2600-7916 | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/196_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10462355 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 14823 | |||||||||||
