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Python 3 Text Processing with NLTK 3 Cookbook
Python 3 Text Processing with NLTK 3 Cookbook

Python 3 Text Processing with NLTK 3 Cookbook: , Second Edition

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Python 3 Text Processing with NLTK 3 Cookbook

Chapter 1. Tokenizing Text and WordNet Basics

In this chapter, we will cover the following recipes:

  • Tokenizing text into sentences
  • Tokenizing sentences into words
  • Tokenizing sentences using regular expressions
  • Training a sentence tokenizer
  • Filtering stopwords in a tokenized sentence
  • Looking up Synsets for a word in WordNet
  • Looking up lemmas and synonyms in WordNet
  • Calculating WordNet Synset similarity
  • Discovering word collocations

Introduction

Natural Language ToolKit (NLTK) is a comprehensive Python library for natural language processing and text analytics. Originally designed for teaching, it has been adopted in the industry for research and development due to its usefulness and breadth of coverage. NLTK is often used for rapid prototyping of text processing programs and can even be used in production applications. Demos of select NLTK functionality and production-ready APIs are available at http://text-processing.com.

This chapter will cover the basics of tokenizing text and using WordNet. Tokenization is a method of breaking up a piece of text into many pieces, such as sentences and words, and is an essential first step for recipes in the later chapters. WordNet is a dictionary designed for programmatic access by natural language processing systems. It has many different use cases, including:

  • Looking up the definition of a word
  • Finding synonyms and antonyms
  • Exploring word relations and similarity
  • Word sense disambiguation for words that have multiple uses and definitions

NLTK includes a WordNet corpus reader, which we will use to access and explore WordNet. A corpus is just a body of text, and corpus readers are designed to make accessing a corpus much easier than direct file access. We'll be using WordNet again in the later chapters, so it's important to familiarize yourself with the basics first.

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Description

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.

What you will learn

  • Tokenize text into sentences, and sentences into words
  • Look up words in the WordNet dictionary
  • Apply spelling correction and word replacement
  • Access the builtin text corpora and create your own custom corpus
  • Tag words with parts of speech
  • Chunk phrases and recognize named entities
  • Grammatically transform phrases and chunks
  • Classify text and perform sentiment analysis
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 26, 2014
Length: 304 pages
Edition : 2nd
Language : English
ISBN-13 : 9781782167853
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Length: 304 pages
Edition : 2nd
Language : English
ISBN-13 : 9781782167853
Category :
Languages :
Tools :

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Table of Contents

11 Chapters
1. Tokenizing Text and WordNet Basics Chevron down icon Chevron up icon
2. Replacing and Correcting Words Chevron down icon Chevron up icon
3. Creating Custom Corpora Chevron down icon Chevron up icon
4. Part-of-speech Tagging Chevron down icon Chevron up icon
5. Extracting Chunks Chevron down icon Chevron up icon
6. Transforming Chunks and Trees Chevron down icon Chevron up icon
7. Text Classification Chevron down icon Chevron up icon
8. Distributed Processing and Handling Large Datasets Chevron down icon Chevron up icon
9. Parsing Specific Data Types Chevron down icon Chevron up icon
A. Penn Treebank Part-of-speech Tags Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(12 Ratings)
5 star 50%
4 star 16.7%
3 star 8.3%
2 star 8.3%
1 star 16.7%
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Sheng-miao Kung Nov 19, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's a GREAT book!
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Luis Felipe Yepez Barrios Feb 15, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
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P.Rotondo Nov 01, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Finally one of the best cookbook I've never read.Author is highly lucid and consistent in his explanation; He leaves nothing to chance and,when required, He delves into the topic.Material is extremely useful, code is very well designed! (I have read many texts on Python where code was catastrophic…) ; Excellent Support.
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rakesh patra Aug 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It’s a very good information and with lots of hands on code. Really useful for ppl who are in there mid journey on NLP research
Amazon Verified review Amazon
Glenn W. Feb 08, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Informative
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