rajspeaks/machine-learning-approach-to-bengali-corpus-tokenization-stemming-pos-tagging-using-bnltk

Machine Learning approach to Bengali Corpus POS Tagging using BNLTK. This is an experimenting project under the mentorship of Prof. Sandipan Ganguly, HIT-K.

Machine Learning approach to Bengali Corpus Tokenization | Stemming | POS Tagging using BNLTK

BNLTK Means Bengali Natural Language Toolkit developed by Asraf Patoary. By using BNLTK, we can tokenize, stemming, tagging parts of speeches categories on Bengali Words.

Installation:

pip install bnltk

Methodology

• First we have installed BNLTK.
• Imported Tokenizers from bnltk & tokenized a Bengali Sentence by splitting into individual words. Then applied the same on a larger Bengali Corpus to tokenize Bengali words.
• Imported BanglaStemmer() from bnltk to apply stemming on Bengali Words. Repeated 2 times the same on different words.
• Downloaded the Datafile from bnltk before moving for further execution.
• Imported PosTagger from bnltk & applied on a Bengali small sentence & tagged each Bengali words into different Parts of Speech categories. Repeated the same 2 times more on larger Bengali Corpora.

Tools & Library requirements:

• Google Colab/Jupyter-Notebook
• BNLTK Library

Reference:

1. https://ashwoolford.github.io/bnltk-documentation/
2. https://github.com/ashwoolford/bnltk

Mentor:

Prof. Sandipan Ganguly

Developer:

Rajdeep Das