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Bengali POS Tagging using Indian Corpus through NLTK. A sample testing to apply POS Tagging under the supervision of Prof. Sandipan Ganguly, HIT-K.

License: GNU General Public License v3.0

Jupyter Notebook 100.00%
bengali bengali-nlp bangla-pos-tagging bangla-postag bengali-pos-tagging bengali-natural-language-processing nlp pos-tagging pos-tagger nltk indian-language natural-language-processing machine-learning rajdeep-das rajspeaks

machine-learning-approach-to-bengali-pos-tagging-using-nltk's Introduction

Machine Learning approach to Bengali POS Tagging using NLTK on Indian-Corpus

Indian corpus is a collection of these Indian Languages: Bengali, Hindi, Marathi, and Telugu language data. NLTK is Natural Language Toolkit Library.

Methodology

  • Here I have imported NLTK(Natural Language Tool Kit).
  • Imported indian corpus from NLTK.
  • Stored that Indian Corpus into 'bangla.pos'.
  • 'bangla.pos' has been stored in a variable 'tagged_set'.
  • Stored the bengali sentences from bengali corpus into 'word_set' variable.
  • Using for loop to count the number of sentences, present in that corpus.

Tools & Library requirements:

  • Google Colab/Jupyter
  • Language: Python
  • NLTK Library

Mentor:

Prof. Sandipan Ganguly

Developer:

Rajdeep Das

Reference:

Click here to read the source article.

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