Python 3 Text Processing with NLTK 3 Cookbook: Over 80 practical recipes on natural language processing
techniques using Python's NLTK 3.0
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Introduction
Natural language processing is used everywhere, from search engines such as Google
or Weotta, to voice interfaces such as Siri or Dragon NaturallySpeaking. Python's Natural
Language Toolkit (NLTK) is a suite of libraries that has become one of the best tools for
prototyping and building natural language processing systems.
Python 3 Text Processing with NLTK 3 Cookbook is your handy and illustrative guide, which
will walk you through many natural language processing techniques in a step-by-step manner.
It will demystify the dark arts of text mining and language processing using the comprehensive
Natural Language Toolkit.
This book cuts short the preamble, ignores pedagogy, and lets you dive right into the
techniques of text processing with a practical hands-on approach.
Get started by learning how to tokenize text into words and sentences, then explore the
WordNet lexical dictionary. Learn the basics of stemming and lemmatization. Discover various
ways to replace words and perform spelling corrections. Create your own corpora and custom
corpus readers, including a MongoDB-based corpus reader. Use part-of-speech taggers to
annotate words. Create and transform chunked phrase trees and named entities using partial
parsing and chunk transformations. Dig into feature extraction and text classification for
sentiment analysis. Learn how to process large amount of text with distributed processing and
NoSQL databases.
This book will teach you all that and more, in a hands-on learn-by-doing manner. Become an
expert in using NLTK for Natural Language Processing with this useful companion.
What this book covers
Chapter 1, Tokenizing Text and WordNet Basics, covers how to tokenize text into sentences
and words, then look up those words in the WordNet lexical dictionary.
Chapter 2, Replacing and Correcting Words, demonstrates various word replacement
and correction techniques, including stemming, lemmatization, and using the Enchant
spelling dictionary
Chapter 3, Creating Custom Corpora, explains how to use corpus readers and create custom
corpora. It also covers how to use some of the corpora that come with NLTK.
Chapter 4, Part-of-speech Tagging, shows how to annotate a sentence of words with
part-of-speech tags, and how to train your own custom part-of-speech tagger.
Chapter 5, Extracting Chunks, covers the chunking process, also known as partial parsing,
which can identify phrases and named entities in a sentence. It also explains how to train
your own custom chunker and create specific named entity recognizers.
Chapter 6, Transforming Chunks and Trees, demonstrates how to transform chunk phrases
and parse trees in various ways.
Chapter 7, Text Classification, shows how to transform text into feature dictionaries, and
how to train a text classifier for sentiment analysis. It also covers multi-label classification
and classifier evaluation metrics.
Chapter 8, Distributed Processing and Handling Large Datasets, discusses how to use
execnet for distributed natural language processing and how to use Redis for storing
large datasets.
Chapter 9, Parsing Specific Data Types, covers various Python modules that are useful
for parsing specific kinds of data, such as datetimes and HTML.
Appendix, Penn Treebank Part-of-speech Tags, shows a table of Treebank part-of-speech
tags, that is a useful reference for Chapter 3, Creating Custom Corpora, and Chapter 4,
Part-of-speech Tagging.
Who this book is for
If you are an intermediate to advanced Python programmer who wants to quickly get to grips
with using NLTK for natural language processing, this is the book for you. It will help if you
are somewhat familiar with basic text processing techniques, such as regular expressions.
Programmers with NLTK experience may learn something new, and students of linguistics
will find it invaluable.