Saman Sarker Joy

I am Saman, a Computer Science & Engineering Graduate student at BRAC University with an interest in Deep Learning, Machine Learning, Natural Language Processing, and Programming Languages. I am currently working as a Research Assistant at Optics and Photonics Research Laboratory, BRAC University, specializing in machine learning and deep learning.

I am a proficient programmer with expertise in a range of languages, including Python, Java, C++, and more.

I have hands-on experience in developing and implementing machine learning algorithms, as well as working with neural networks and deep learning frameworks like TensorFlow and PyTorch.

Check out my GitHub page.

The best way to contact me is by email: saman.sarker.joy@gmail.com.

News

Jul 27, 2024	Appointed as Research Assistant at Optics and Photonics Research Laboratory, Brac University.
Jan 30, 2024	My paper Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT Semantic Embeddings K-Means-Infused CRF Model is available as a prepreint at arXiv.
Oct 18, 2023	Graduated with a Bachelor of Science in Computer Science Engineering degree from BRAC University with the Highest Distinction and being included in VC’s List.
Sep 25, 2023	My paper BanglaClickBERT: Bangla Clickbait Detection from News Headlines using Domain Adaptive BanglaBERT and MLP Techniques has been accepted at ALTA 2023 Conference.
May 25, 2023	My Bachelor’s thesis Research on the latest trends in Bangla Named Entity Recognition defended at BRAC University.

Selected Publications

BanglaClickBERT: Bangla Clickbait Detection from News Headlines using Domain Adaptive BanglaBERT and MLP Techniques

Saman Sarker Joy, Tanusree Das Aishi, Naima Tahsin Nodi, and Annajiat Alim Rasel

In Proceedings of The 21th Annual Workshop of the Australasian Language Technology Association, 2023

Abs Bib PDF

News headlines or titles that deliberately persuade readers to view a particular online content are referred to as clickbait. There have been numerous studies focused on clickbait detection in English language, compared to that, there have been very few researches carried out that address clickbait detection in Bangla news headlines. In this study, we have experimented with several distinctive transformers models, namely BanglaBERT and XLM-RoBERTa. Additionally, we introduced a domain-adaptive pretrained model, BanglaClickBERT. We conducted a series of experiments to identify the most effective model. The dataset we used for this study contained 15,056 labeled and 65,406 unlabeled news headlines; in addition to that, we have collected more unlabeled Bangla news headlines by scraping clickbait-dense websites making a total of 1 million unlabeled news headlines in order to make our BanglaClickBERT. Our approach has successfully surpassed the performance of existing state-of-the-art technologies providing a more accurate and efficient solution for detecting clickbait in Bangla news headlines, with potential implications for improving online content quality and user experience.
@inproceedings{saman2023banglaclickbert, author = {Joy, Saman Sarker and Aishi, Tanusree Das and Nodi, Naima Tahsin and Rasel, Annajiat Alim}, booktitle = {Proceedings of The 21th Annual Workshop of the Australasian Language Technology Association}, title = {BanglaClickBERT: Bangla Clickbait Detection from News Headlines using Domain Adaptive BanglaBERT and MLP Techniques}, year = {2023}, archiveprefix = {ALTA 2023}, primaryclass = {cs.LG}, }