Natural Language Processing Fundamentals with NLTK
To provide a comprehensive guide to Natural Language Processing (NLP) concepts and techniques using Python and NLTK, aimed at beginners and intermediate learners who want to gain practical experience with core NLP preprocessing and feature extraction methods.
This repository contains Jupyter notebooks and Python scripts that cover foundational concepts and practical implementations of NLP preprocessing techniques. Each topic is accompanied by clear explanations and code examples using the Natural Language Toolkit (NLTK) library. By exploring this repository, users will gain insights into various text processing tasks essential for NLP projects, including:
- Tokenization: Understanding the basics of splitting text into meaningful units (tokens) and practical examples using NLTK.
- Text Preprocessing: Techniques such as stemming, lemmatization, and stopword removal to clean and prepare raw text for analysis.
- Parts of Speech (POS) Tagging: Using NLTK to assign grammatical tags to each token for syntactic analysis.
- Named Entity Recognition (NER): Identifying and classifying named entities like persons, organizations, and locations in text data.
- Encoding Techniques: An exploration of encoding methods like One Hot Encoding (OHE) and Bag of Words (BOW), discussing their advantages and disadvantages.
- N-Grams and Feature Engineering: Implementing and using N-Grams and N-Gram-based Bag of Words with NLTK for context-aware text features.
This repository is structured to provide hands-on experience with NLP and help users understand the trade-offs and considerations of various preprocessing techniques in real-world applications.
Each notebook includes code snippets for practical implementation.