📘 Natural Language Processing (NLP) Fundamentals

• This repository contains beginner-friendly Jupyter notebooks demonstrating essential Natural Language Processing (NLP) techniques using the NLTK library.

📂 Contents

1. Parts of Speech Tagging

• File: NLP( parts of speech) .ipynb

• Tokenization of text into words.

• Using NLTK for POS (Part-of-Speech) tagging.

• Identifying nouns, verbs, adjectives, adverbs, etc.

• Understanding grammatical structure of sentences.

Topics Covered

• Word Tokenization
• POS Tagging
• NLTK Installation and Downloads

2. Stop Word Removal

• File: StopWord(NLP).ipynb

• Introduction to stop words.

• Removing common words (such as is, the, and, a) from text.

• Preparing text for NLP applications.

Topics Covered

• Tokenization
• Stop Words
• Text Cleaning
• NLTK Stopwords Corpus

3. Stop Word Removal and Stemming

• File: StopWord removal & Stemming(NLP).ipynb

• Removing unnecessary words from text.

• Applying stemming to reduce words to their root forms.

• Understanding how stemming improves text preprocessing.

Topics Covered

• Word Tokenization
• Stop Word Removal
• Stemming
• Text Preprocessing

4. Lemmatization

• File: Lemmatization(NLP).ipynb

• Converting words into their dictionary (base) form.

• Understanding the difference between stemming and lemmatization.

• Using WordNetLemmatizer from NLTK.

Topics Covered

• Lemmatization
• WordNetLemmatizer
• Morphological Analysis

🛠️ Technologies Used

• Python 3

• Jupyter Notebook

• NLTK (Natural Language Toolkit)

• Install NLTK:

• pip install nltk

• Download required datasets:

• import nltk

• nltk.download('punkt')

• nltk.download('stopwords')

• nltk.download('wordnet')

• nltk.download('averaged_perceptron_tagger')

📖 Learning Objectives

• After completing these notebooks, you will be able to:

• Tokenize text data.

• Remove stop words effectively.

• Perform stemming and lemmatization.

• Apply Part-of-Speech tagging.

• Understand basic NLP preprocessing techniques.

🚀 Applications

• These techniques are widely used in:

• Text Classification

• Sentiment Analysis

• Chatbots

• Information Retrieval

• Search Engines

• Machine Translation

• Recommendation Systems

Author

Priya Singh Natural Language Processing Practice using Python and NLTK