Natural Language Processing: Python and NLTK

Learn to build expert NLP and machine learning projects using NLTK and other Python libraries

About This Book

• Break text down into its component parts for spelling correction, feature extraction, and phrase transformation
• Work through NLP concepts with simple and easy-to-follow programming recipes
• Gain insights into the current and budding research topics of NLP

Who This Book Is For

If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable.

What You Will Learn

• The scope of natural language complexity and how they are processed by machines
• Clean and wrangle text using tokenization and chunking to help you process data better
• Tokenize text into sentences and sentences into words
• Classify text and perform sentiment analysis
• Implement string matching algorithms and normalization techniques
• Understand and implement the concepts of information retrieval and text summarization
• Find out how to implement various NLP tasks in Python

In Detail

Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it’s becoming imperative that computers comprehend all major natural languages.

The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.

The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods.

The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:

• NTLK essentials by Nitin Hardeniya
• Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins
• Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur

Style and approach

This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You’ll learn to create effective NLP and machine learning projects using Python and NLTK.

Table of Contents

Part 1. Module 1
Chapter 1. Introduction to Natural Language Processing
Chapter 2. Text Wrangling and Cleansing
Chapter 3. Part of Speech Tagging
Chapter 4. Parsing Structure in Text
Chapter 5. NLP Applications
Chapter 6. Text Classification
Chapter 7. Web Crawling
Chapter 8. Using NLTK with Other Python Libraries
Chapter 9. Social Media Mining in Python
Chapter 10. Text Mining at Scale
Part 2. Module 2
Chapter 1. Tokenizing Text and WordNet Basics
Chapter 2. Replacing and Correcting Words
Chapter 3. Creating Custom Corpora
Chapter 4. Part-of-speech Tagging
Chapter 5. Extracting Chunks
Chapter 6. Transforming Chunks and Trees
Chapter 7. Text Classification
Chapter 8. Distributed Processing and Handling Large Datasets
Chapter 9. Parsing Specific Data Types
Appendix A. Penn Treebank Part-of-speech Tags
Part 3. Module 3
Chapter 1. Working with Strings
Chapter 2. Statistical Language Modeling
Chapter 3. Morphology – Getting Our Feet Wet
Chapter 4. Parts-of-Speech Tagging – Identifying Words
Chapter 5. Parsing – Analyzing Training Data
Chapter 6. Semantic Analysis – Meaning Matters
Chapter 7. Sentiment Analysis – I Am Happy
Chapter 8. Information Retrieval – Accessing Information
Chapter 9. Discourse Analysis – Knowing Is Believing
Chapter 10. Evaluation of NLP Systems – Analyzing Performance
Appendix B. Bibliography