This repository contains a comprehensive guide to natural language processing (NLP) text processing and analysis. It covers the following topics:
- Text Pre-processing
- Text Representation
- Word2Vec
- Text Classification
- POS Tagging
Text pre-processing is the first and crucial step in NLP. It involves cleaning and transforming the raw text data into a format that can be easily analyzed. This section provides a detailed explanation of text pre-processing techniques, including:
- Lowercasing
- Removing punctuations and special characters
- Removing stop words
- Stemming and Lemmatization
- Removing HTML tags
In NLP, text data needs to be transformed into numerical vectors for analysis. This section provides an overview of text representation techniques, including:
- One-hot encoding
- Term frequency-inverse document frequency (TF-IDF)
- Word Embeddings
Word2Vec is a widely used word embedding technique that represents words in a high-dimensional vector space. This section provides a step-by-step guide to training and using Word2Vec models.
Text classification is a popular NLP task that involves classifying text data into different categories based on its content. This section provides an in-depth explanation of text classification, including:
- Types of text classification
- Text feature extraction
- Model selection and training
- Evaluation metrics
POS (part-of-speech) tagging is the process of marking each word in a text with its corresponding part of speech. This section provides an introduction to POS tagging, including:
- What is POS tagging
- POS tag sets
- POS tagging algorithms
This repository provides a comprehensive guide to NLP text processing and analysis, covering the essential topics in the field. Whether you're a beginner or an experienced NLP practitioner, this repository will help you improve your skills and knowledge.