Papers by Mohammad Ruhul Amin
A Study on Using Semantic Word Associations to Predict the Success of a Novel (2021.starsem-1)
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Syeda Jannatus Saba, Biddut Sarker Bijoy, Henry Gorelick, Sabir Ismail, Md Saiful Islam, Mohammad Ruhul Amin
| Challenge: | Existing methods for book success prediction are not effective. |
| Approach: | They propose to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embeddment for a set of semantically linked word clusters. |
| Outcome: | The proposed method outperforms the previous methods for book success prediction. |
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts (2022.lrec-1)
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Nauros Romim, Mosahed Ahmed, Md Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, Mohammad Ruhul Amin
| Challenge: | Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS) due to the massive amount of user-generated content, modern machine learning techniques are feasible and cost-effective to tackle this problem. |
| Approach: | They propose to use a large manually labeled Bangla HS dataset to train generalizable models. |
| Outcome: | The proposed dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than existing Bangla HS datasets. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models (2025.emnlp-industry)
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Nafew Azim, Abrar Ur Alam, Hasan Bin Omar, Abdullah Mohammad Muntasir Adnan Jami, Jawad Ibn Ahad, Muhammad Rafsan Kabir, Md. Ismail Hossain, Fuad Rahman, Mohammad Ruhul Amin, Shafin Rahman, Nabeel Mohammed
| Challenge: | Large Language Models excel at complex reasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. |
| Approach: | They propose a framework that fully automates large language models' pipeline construction using reinforcement learning. |
| Outcome: | Experimental results show that autoDSPy outperforms DSPy benchmarks in accuracy gains and time. |
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts (2021.findings-emnlp)
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| Challenge: | Bangla is the sixth most spoken language worldwide and the second Indo-Aryan language after Hindi. |
| Approach: | They propose an annotated sentiment analysis dataset made of informally written Bangla texts. |
| Outcome: | The proposed dataset is compared with neural networks and pretrained models . it shows that hand-crafted lexical features provide superior performance than neural networks . |
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025.findings-naacl)
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Md. Motahar Mahtab, Faisal Ahamed Khan, Md. Ekramul Islam, Md. Shahad Mahmud Chowdhury, Labib Imam Chowdhury, Sadia Afrin, Hazrat Ali, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin
| Challenge: | In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets. |
| Approach: | They propose to use Bangla as a language to create the most extensive human-annotated and validated Bangla NLP dataset. |
| Outcome: | The proposed method outperforms existing methods on Bangla NER datasets and performs competitively on English datasets. |