Annotated Bangla Natural Language Processing (BNLP) Using Python and Machine Learning

Abstract

Implementing machine learning models to Natural Language Processing (NLP) would be difficult if it has a shortage of thorough research assessing machine-based tools and well-established corpus. Bangla language has only a few annotated datasets and corpus tasks for NLP. This paper addresses the significance of filling in the gaps for the advancement of BNLP. This paper offers a thorough method for creating and assessing Python-based Natural Language Processing (NLP) tools for the Bengali language. The study of natural language processing focuses on how computers can be programmed to recognize, comprehend, and manipulate natural language speech or text for practical purposes. The study entails a thorough process for developing and testing NLP tools for the Bangla language based on Python and Machine Learning. It centers on how computers can be taught to perform tasks like Named Entity Recognition, Tokenization, Part-of-Speech (POS) Tagging, and Sentiment Analysis to comprehend Bengali text. The study commences with the introduction of a thoroughly annotated corpus that forms the basis for these activities and is intended to encompass a broad spectrum of language situations and structures. The authors created datasets that have been annotated for Bangla NLP tasks to put into practice Bengali-specific NLP and machine learning methods based on Python. Finally, the authors assessed these methods' efficacy and performance in use cases like as text categorization, machine translation, and analysis of sentiment. Moreover, the study hopes to encourage future research and development in Bangla NLP by making these tools and resources open source, encouraging cooperation and creativity. This project aims to aid the larger NLP community by offering a strong basis for applications like machine translation and sentiment analysis in the Bangla language.