https://github.com/md-emon-hasan/nlp-codebasics

Collection of basic Natural Language Processing examples that cover essential techniques like tokenization, text representation, and text classification.
https://github.com/md-emon-hasan/nlp-codebasics

bag-of-words bow gensim gensim-word2vec lematization nlp nlp-library nlp-machine-learning nltk nltk-python python3 spacy text-classification text-processing tokenization

Last synced: 6 months ago
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Collection of basic Natural Language Processing examples that cover essential techniques like tokenization, text representation, and text classification.

• Host: GitHub
• URL: https://github.com/md-emon-hasan/nlp-codebasics
• Owner: Md-Emon-Hasan
• License: apache-2.0
• Created: 2024-12-11T14:06:47.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2025-01-17T17:51:03.000Z (about 1 year ago)
• Last Synced: 2025-09-04T00:43:16.945Z (7 months ago)
• Topics: bag-of-words, bow, gensim, gensim-word2vec, lematization, nlp, nlp-library, nlp-machine-learning, nltk, nltk-python, python3, spacy, text-classification, text-processing, tokenization
• Language: Jupyter Notebook
• Homepage:
• Size: 21.1 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Roadmap: Roadmap/Roadmap.md

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README

          
# NLP CodeBasics

This repository provides fundamental code examples and techniques for Natural Language Processing (NLP). It covers essential concepts, tools, and methods such as tokenization, part-of-speech tagging, named entity recognition, and text classification. The project employs popular Python libraries like **spaCy**, **NLTK**, and **Gensim** to implement various NLP tasks.

Key Features:

- Tokenization with **spaCy**

- **Part-of-Speech Tagging** and **Named Entity Recognition** (NER)

- Text representation with **TF-IDF**, **Word Vectors**, and **Word2Vec**

- **Text Classification** using **spaCy** and **Gensim**

- Comparative studies: **spaCy vs NLTK**

- Advanced NLP techniques for feature extraction and preprocessing