Python 3 Text Processing with NLTK 3 Cookbook : Over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0

Jacob Perkins

ISBN 10: 1782167854 / ISBN 13: 9781782167853

Published by Packt Publishing, 2014

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0About This BookBreak text down into its component parts for spelling correction, feature extraction, and phrase transformationLearn how to do custom sentiment analysis and named entity recognitionWork through the natural language processing concepts with simple and easy-to-follow programming recipesIn DetailThis book will show you the essential techniques of text and language processing. Starting with tokenization, stemming, and the WordNet dictionary, you'll progress to part-of-speech tagging, phrase chunking, and named entity recognition. You'll learn how various text corpora are organized, as well as how to create your own custom corpus. Then, you'll move onto text classification with a focus on sentiment analysis. And because NLP can be computationally expensive on large bodies of text, you'll try a few methods for distributed text processing. Finally, you'll be introduced to a number of other small but complementary Python libraries for text analysis, cleaning, and parsing.This cookbook provides simple, straightforward examples so you can quickly learn text processing with Python and NLTK.What You Will LearnTokenize text into sentences, and sentences into wordsLook up words in the WordNet dictionaryApply spelling correction and word replacementAccess the built-in text corpora and create your own custom corpusTag words with parts of speechChunk phrases and recognize named entitiesGrammatically transform phrases and chunksClassify text and perform sentiment analysis.

Seller Inventory # 9781782167853

Bibliographic Details

• Title: Python 3 Text Processing with NLTK 3 Cookbook : Over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0
• Publisher: Packt Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 1782167854
• ISBN 13: 9781782167853
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 235W x 191H x 17D millimeters 235 millimeters width by 191 millimeters height by 17 millimeters depth
• Weight: 572 grams

About this title

Synopsis

This is a recipe-based Cookbook, where each chapter is divided into individual recipes, each presenting a new concept, technique or algorithm. Many of the recipes build upon or make use of techniques from previous recipes and chapters, so it is recommend that you start from the beginning, and do not skip ahead, unless you re already familiar with the previous content.
This book is intended for Python programmers interested in learning how to do natural language processing. Maybe you ve learned the limits of regular expressions the hard way, or you ve realized that human language cannot be deterministically parsed like a computer language. Perhaps you have more text than you know what to do with, and need automated ways to analyze and structure that text. This Cookbook will show you how to train and use statistical language models to process text in ways that are practically impossible with standard programming tools. A basic knowledge of Python and the basic text processing concepts is expected. Some experience with regular expressions will also be helpful.

About the Author

Jacob Perkins is the cofounder and CTO of Weotta, a local search company. Weotta uses NLP and machine learning to create powerful and easy-to-use natural language search for what to do and where to go. He is the author of Python Text Processing with NLTK 2.0 Cookbook, Packt Publishing, and has contributed a chapter to the Bad Data Handbook, O'Reilly Media. He writes about NLTK, Python, and other technology topics at http: //streamhacker.com. To demonstrate the capabilities of NLTK and natural language processing, he developed http: //text-processing.com, which provides simple demos and NLP APIs for commercial use. He has contributed to various open source projects, including NLTK, and created NLTK-Trainer to simplify the process of training NLTK models. For more information, visit https: //github.com/japerk/nltk-trainer.