Browsing by Author "Das, Shudipta"

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Automated Phrasal Verb and Key-Phrase Checking with LSTM-Based Attention Mechanism
(MDPI Publications, 2023-09-13) Tusher, Abdur Nur; Anjum, Anika; Anjum, Anika; Das, Shudipta; Sammy, Mst. Sakira Rezowana; Hossain, Dr. Gahangir
Text prediction and classification are crucial tasks in modern Natural Language Processing (NLP) techniques. Long short-term memory (LSTM), a type of Recurrent Neural Network (RNN), is well-known for its outstanding performance in text classification. Phrasal verbs, also known as Bagdhara in Bangla, play a vital role in making language more expressive and poetic in any language, including Bangla. These two or three-word phrases help us convey our emotions and thoughts more effectively. However, determining whether a phrase is a phrasal verb and appropriate for a given context can be challenging for writers, poets, and the general public. To address this issue, an automatic system capable of identifying and using phrasal verbs is necessary. In this study, we propose a system that can instantly and accurately predict phrasal verbs using the LSTM algorithm, a part of the RNN, and an attention mechanism. Our system achieved an overall phrasal verb prediction accuracy of 78.63%.
Zero- and Few-Shot Prompting with LLMs:
(Daffodil International University, 2024-05-30) Hasan, Md. Arid; Das, Shudipta; Anjum, Afiyat; Alam, Firoj; Anjum, Anika; Sarke, Avijit; Noori, Sheak Rashed Haider
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community.
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis
(Scopus, 2024) Hasan, Md. Arid; Das, Shudipta; Anjum, Afiyat; Alam, Firoj; Anjum, Anika; Sarker, Avijit; Noori, Sheak Rashed Haider
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community.
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis
(Elsevier, 2024-05-25) Hasan, Md. Arid; Das, Shudipta; Anjum, Afiyat; Alam, Firoj; Anjum, Anika; Sarker, Avijit; Sheak Rashed Haider; Noori, Sheak Rashed Haider
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare. While there have been significant advancements in sentiment analysis for widely spoken languages, low-resource languages, such as Bangla, remain largely under-researched due to resource constraints. Furthermore, the recent unprecedented performance of Large Language Models (LLMs) in various applications highlights the need to evaluate them in the context of low-resource languages. In this study, we present a sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and Facebook comments. We also investigate zero- and few-shot in-context learning with several language models, including Flan-T5, GPT-4, and Bloomz, offering a comparative analysis against fine-tuned models. Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community.