- Author:
- Jacob Perkins
Quickly get to grips with Natural Language Processing - with Tokenizing Text, WordNet, and beyond Learn how to replace and correct words to improve search engine results Categorize documents and pieces of text and assign class labels Part of Packt's Cookbook series: Each recipe is a carefully organized sequence of instructions to complete the task as efficiently as possible In Detail Natural Language Processing is used everywhere - in search engines, spell checkers, mobile phones, computer games - even your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing. You want to employ nothing less than the best techniques in Natural Language Processing - and this book is your answer. Python Text Processing with NLTK 2.0 Cookbook is your handy and illustrative guide, which will walk you through all the Natural Language Processing techniques in a step-by-step manner. It will demystify the advanced features of text analysis and text mining using the comprehensive NLTK suite. What you will learn from this book Learn Text categorization and Topic identification Use Stemming and Lemmatization to go beyond the usual spell checker Replace negations with antonyms in your text Tokenize words into lists of sentences and words, and gain an insight into WordNet Train and manipulate different types of classifiers Automatically correct spelling with Enchant Translate text using Babelfish Approach The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward.. Who this book is written for This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NLTK will also find it useful. Students of linguistics will find it invaluable.