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