
Natural Language Processing Fundamentals
Build intelligent applications that can interpret the human language to deliver impactful results
Packt Publishing
Published on 30. March 2019
Book
Paperback/Softback
374 pages
978-1-78995-404-3 (ISBN)
Description
Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems.
Key Features
Assimilate key NLP concepts and terminologies
Explore popular NLP tools and techniques
Gain practical experience using NLP in application code
Book DescriptionIf NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.
You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots.
By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
What you will learn
Obtain, verify, and clean data before transforming it into a correct format for use
Perform data analysis and machine learning tasks using Python
Understand the basics of computational linguistics
Build models for general natural language processing tasks
Evaluate the performance of a model with the right metrics
Visualize, quantify, and perform exploratory analysis from any text data
Who this book is forNatural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.
Key Features
Assimilate key NLP concepts and terminologies
Explore popular NLP tools and techniques
Gain practical experience using NLP in application code
Book DescriptionIf NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.
You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots.
By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
What you will learn
Obtain, verify, and clean data before transforming it into a correct format for use
Perform data analysis and machine learning tasks using Python
Understand the basics of computational linguistics
Build models for general natural language processing tasks
Evaluate the performance of a model with the right metrics
Visualize, quantify, and perform exploratory analysis from any text data
Who this book is forNatural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 20 mm
Weight
697 gr
ISBN-13
978-1-78995-404-3 (9781789954043)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Ghosh Sohom Ghosh
Natural Language Processing Fundamentals
Build intelligent applications that can interpret the human language to deliver impactful results
E-Book
03/2019
1st Edition
Packt Publishing
from
€46.39
Available for download
Persons
Dwight Gunning is a data scientist at FINRA, a financial services regulator in the USA. He has extensive experience in Python-based machine learning and hands-on experience with the most popular NLP tools such as NLTK, gensim, and spacy. Sohom Ghosh is a passionate data detective with expertise in Natural Language Processing. He has worked extensively in the Data Science arena with specialization in Deep Learning based Text Analytics, NLP & Recommendation Systems. He has published various articles in several international conferences and journals.
Content
Table of Contents
General Introduction to NLP
Extraction Methods from Unstructured Text
Building a Simple Classifier
Collecting Text Data
Topic Modelling
Text Summarization and Text Generation
Vector Representation
Sentiment Analysis
Language Detection using Neural Networks
General Introduction to NLP
Extraction Methods from Unstructured Text
Building a Simple Classifier
Collecting Text Data
Topic Modelling
Text Summarization and Text Generation
Vector Representation
Sentiment Analysis
Language Detection using Neural Networks