Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications

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Text Classification and POS Tagging Using NLTK

The Natural Language Toolkit (NLTK) is a Python library for handling natural language processing (NLP) tasks, ranging from segmenting words or sentences to performing advanced tasks, such as parsing grammar and classifying text. NLTK provides several modules and interfaces to work on natural language, useful for tasks such as document topic identification, parts of speech (POS) tagging, sentiment analysis, and so on. For experimentation with various NLP tasks, NLTK also includes modules for a wide range of text corpora, from basic text collections to tagged and structured texts, such as WordNet. While the NLTK library provides a vast set of APIs, we will only cover the most important aspects that are commonly used in practical NLP applications.

We will cover the following topics in this chapter:

Installing NLTK and its modules
Text...

Key benefits

• Weave neural networks into linguistic applications across various platforms

• Perform NLP tasks and train its models using NLTK and TensorFlow

• Boost your NLP models with strong deep learning architectures such as CNNs and RNNs

Description

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.

Who is this book for?

Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

What you will learn

•Implement semantic embedding of words to classify and find entities

•Convert words to vectors by training in order to perform arithmetic operations

•Train a deep learning model to detect classification of tweets and news

•Implement a question-answer model with search and RNN models

•Train models for various text classification datasets using CNN

•Implement WaveNet a deep generative model for producing a natural-sounding voice

•Convert voice-to-text and text-to-voice

•Train a model to convert speech-to-text using DeepSpeech

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Hands-On Natural Language Processing with Python

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Natural Language Processing with TensorFlow

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Natural Language Processing and Computational Linguistics

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Total $ 131.97

Russell Jurney Jul 07, 2019

This book’s coverage of things you can do to text data using natural language processing is excellent! It is quite a menu to choose from. It does assume you know Python but it says so at the beginning so the negative reviews aren’t valid.

Amazon Verified review

Santhosh Jan 29, 2020

Lacks depth in the discussions, though the book covers important topics in NLP.Not worth the price.I supplement the book with Internet resources.

Gary Woodfine Feb 24, 2019

I don't think I can quite put into words, just how disappointed I am with this book. I feel the authors did an extremely poor job of attempting to explain this really interesting subject. For the most part, one might get the impression that authors took snippets from other books on the subject and tried to weave it into their book.I have had to re-read several chapters of the book, several times in order to try understand what it is the authors are trying to explain. I am really interested in the subject, but in my opinion this book, has actually made it more difficult for me to understand the subject!It may of course, just be me, but I don't think this book offers a hands on approach at all

Edward Nelson Apr 11, 2019

I got to Chapter 4, where the authors begin to tackle neural network NLP models. The main code example in the book has errors and is incomplete. The authors use advanced functions such as a generator and name spaces, with no explanation (and a decent programmer would have trouble figuring out what is going, even after consulting information about the commands used on the Internet). So, I thought, they should have a working example in the code base for the chapter. What I found instead was a mess. I found no code for the only real example of code in the chapter. I found code that belonged under subject headings in other chapters. Of 10 files, I found only 3 with passing relevance to the chapter. These are incomplete (e.g. using data files the whereabouts of which I cannot determine) and are not code that is discussed in the chapter. I haven't taken a close look at subsequent chapters, but suspect, from a quick look through the code base and the fact that code from Ch 4 belonged in other sections of the book, that the situation is unlikely to be better there. I complained to the publisher and their solution (or more likely the authors') was to publish the code on GitHub. However, the GitHub code for chapter 4 is identical to the code with which I had problems (I did a diff across all the files).

Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications

What do you get with Print?

Hands-On Natural Language Processing with Python

Text Classification and POS Tagging Using NLTK

Installing NLTK and its modules

Text preprocessing and exploratory analysis

Tokenization

Exploratory analysis of text

POS tagging

What is POS tagging?

Training a sentiment classifier for movie reviews

Training a bag-of-words classifier

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Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the 5 authors

FAQs