Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer

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Tidying your Text

Data cleaning is one of the most important and time-consuming tasks when it comes to natural language processing (NLP):

"There's the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data."

– Kaggle founder and CEO Anthony Goldbloom in a Verge Interview

In this chapter, we will discuss some of the most common text pre-processing ideas. This task is universal, tedious, and unavoidable. Most people working in data science or NLP understand that it's an underrated value addition. Some of these tasks don't work well in isolation but have a powerful effect when used in the right combination and order. This chapter will introduce several new words and tools, since the field has a rich history from two worlds. It borrows from both traditional NLP and machine learning. We&apos...

Key benefits

A no-math, code-driven programmer’s guide to text processing and NLP

Get state of the art results with modern tooling across linguistics, text vectors and machine learning

Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch

Description

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.

What you will learn

Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus

Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering

Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch

Using an NLP project management Framework for estimating timelines and organizing your project into stages

Hack and build a simple chatbot application in 30 minutes

Deploy an NLP or machine learning application using Flask as RESTFUL APIs

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

$43.99

Artificial Intelligence and Machine Learning Fundamentals

$32.99

Natural Language Processing with Python Quick Start Guide

$32.99

Total $ 109.97

Feature	Spacy	NLTK	CoreNLP
Native Python support/API	Y	Y	Y
Multi-language support	Y	Y	Y
Tokenization	Y	Y	Y
Part-of-speech tagging	Y	Y	Y
Sentence segmentation	Y	Y	Y
Dependency parsing	Y	N	Y
Entity recognition	Y	Y	Y
Integrated word vectors	Y	N	N
Sentiment analysis	Y	Y	Y
Coreference resolution	N	N	Y

Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer

What do you get with Print?

Natural Language Processing with Python Quick Start Guide

Tidying your Text

Bread and butter – most common tasks

Tokenization

Intuitive – split by...

Stemming and lemmatization

spaCy compared with NLTK and CoreNLP

Correcting spelling

Cleaning a corpus with FlashText

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Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer

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Description

Who is this book for?

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What do you get with Print?

Contact Details

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Table of Contents

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