Natural Language Processing with Python Cookbook: Over 60 recipes to implement text analytics solutions using deep learning principles
Learn the tricks and tips that will help you design Text Analytics solutions
About This Book
- Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python
- Use dictionaries to create your own named entities using this easy-to-follow guide
- Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing
Who This Book Is For
This book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended.
What You Will Learn
- Explore corpus management using internal and external corpora
- Learn WordNet usage and a couple of simple application assignments using WordNet
- Operate on raw text
- Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more
- Understand regular expressions for pattern matching
- Learn to use and write your own POS taggers and grammars
- Learn to evaluate your own trained models
- Explore Deep Learning techniques in NLP
- Generate Text from Nietzsche's writing using LSTM
- Utilize the BABI dataset and LSTM to model episodes
Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora.
This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques.
By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python.
Style and Approach
This book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Table of Contents
Chapter 1: Corpus and WordNet
Chapter 2: Raw Text, Sourcing, and Normalization
Chapter 3: Pre-Processing
Chapter 4: Regular Expressions
Chapter 5: POS Tagging and Grammars
Chapter 6: Chunking, Sentence Parse, and Dependencies
Chapter 7: Information Extraction and Text Classification
Chapter 8: Advanced NLP Recipes
Chapter 9: Applications of Deep Learning in NLP
Chapter 10: Advanced Applications of Deep Learning in NLP