The Natural Language Processing Workshop
Confidently design and build your own NLP projects with this easy-to-understand practical guide
Published on 17. August 2020
Book
Paperback/Softback
452 pages
978-1-80107-781-1 (ISBN)
Description
Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data
Key Features
Get familiar with key natural language processing (NLP) concepts and terminology
Explore the functionalities and features of popular NLP tools
Learn how to use Python programming and third-party libraries to perform NLP tasks
Book DescriptionDo you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before?
With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.
The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.
By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.What you will learn
Obtain, verify, clean and transform text data into a correct format for use
Use methods such as tokenization and stemming for text extraction
Develop a classifier to classify comments in Wikipedia articles
Collect data from open websites with the help of web scraping
Train a model to detect topics in a set of documents using topic modeling
Discover techniques to represent text as word and document vectors
Who this book is forThis book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
Key Features
Get familiar with key natural language processing (NLP) concepts and terminology
Explore the functionalities and features of popular NLP tools
Learn how to use Python programming and third-party libraries to perform NLP tasks
Book DescriptionDo you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before?
With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.
The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.
By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.What you will learn
Obtain, verify, clean and transform text data into a correct format for use
Use methods such as tokenization and stemming for text extraction
Develop a classifier to classify comments in Wikipedia articles
Collect data from open websites with the help of web scraping
Train a model to detect topics in a set of documents using topic modeling
Discover techniques to represent text as word and document vectors
Who this book is forThis book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-80107-781-1 (9781801077811)
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
Persons
Rohan Chopra graduated from Vellore Institute of Technology with a bachelors degree in computer science. Rohan has an experience of more than 2 years in designing, implementing, and optimizing end-to-end deep neural network systems. His research is centered around the use of deep learning to solve computer vision-related problems and has hands-on experience working on self-driving cars. He is a data scientist at Absolutdata. Aniruddha M. Godbole is a data science consultant with inter-disciplinary expertise in computer science, applied statistics, and finance. He has a master's degree in data science from Indiana University, USA, and has done MBA in finance from the National Institute of Bank Management, India. He has authored papers in computer science and finance and has been an occasional opinion pages contributor to Mint, which is a leading business newspaper in India. He has fifteen years of experience. Nipun Sadvilkar is a senior data scientist at US healthcare company leading a team of data scientists and subject matter expertise to design and build the clinical NLP engine to revamp medical coding workflows, enhance coder efficiency, and accelerate revenue cycle. He has experience of more than 3 years in building NLP solutions and web-based data science platforms in the area of healthcare, finance, media, and psychology. His interests lie at the intersection of machine learning and software engineering with a fair understanding of the business domain. He is a member of the regional and national python community. He is author of pySBD - an NLP open-source python library for sentence segmentation which is recognized by ExplosionAI (spaCy) and AllenAI (scispaCy) organizations. Muzaffar Bashir Shah is a software developer with vast experience in machine learning, natural language processing (NLP), text analytics, and data science. He holds a masters degree in computer science from the University of Kashmir and is currently working in a Bangalore based startup named Datoin. Sohom Ghosh is a passionate data detective with expertise in natural language processing. He has worked extensively in the data science arena with a specialization in deep learning-based text analytics, NLP, and recommendation systems. He has publications in several international conferences and journals. Dwight Gunning is a data scientist at FINRA, a financial services regulator in the US. 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.
Content
Table of Contents
Introduction to Natural Language Processing
Feature Extraction Methods
Developing a Text Classifier
Collecting Data with Web Scraping and APIs
Topic Modeling
Vector Representation
Text Generation and Summarization
Sentiment Analysis
Introduction to Natural Language Processing
Feature Extraction Methods
Developing a Text Classifier
Collecting Data with Web Scraping and APIs
Topic Modeling
Vector Representation
Text Generation and Summarization
Sentiment Analysis