Scopus Indexed Publications

Paper Details

Analysis of Bangla Transformation of Sentences Using Machine Learning
Rajesh Kumar Das, Dr. Sheak Rashed Haider Noori, Khadijatul Kobra, Moshfiqur Rahman Ajmain, Ms. Sharun Akter Khushbu, SAMRINA SARKAR SAMMI,

In many languages, various language processing tools have been developed. The work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. The most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%.

"Formation of sentence Bangla NLP Supervised Machine learning Decision Tree Classifier Sorol Bakko Jotil Bakko Jougik Bakko"
Journal or Conference Name
Lecture Notes in Networks and Systems
Publication Year