Get well-versed with traditional as well as modern natural language processing concepts and techniques
▶Book Description
Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding.
This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own.
By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
▶What You Will Learn
⦁Understand how NLP powers modern applications
⦁Explore key NLP techniques to build your natural language vocabulary
⦁Transform text data into mathematical data structures and learn how to improve text mining models
⦁Discover how various neural network architectures work with natural language data
⦁Get the hang of building sophisticated text processing models using machine learning and deep learning
⦁Check out state-of-the-art architectures that have revolutionized research in the NLP domain
▶Key Features
⦁Perform various NLP tasks to build linguistic applications using Python libraries
⦁Understand, analyze, and generate text to provide accurate results
⦁Interpret human language using various NLP concepts, methodologies, and tools
▶Who This Book Is For
This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.
▶What this book covers
⦁ Chapter 1, Understanding the Basics of NLP, will introduce you to the past, present, and future of NLP research and applications.
⦁ Chapter 2, NLP Using Python, will gently introduce you to the Python libraries that are used frequently in NLP and that we will use later in the book.
⦁ Chapter 3, Building Your NLP Vocabulary, will introduce you to methodologies for natural language data cleaning and vocabulary building.
⦁ Chapter 4, Transforming Text into Data Structures, will discuss basic syntactical techniques for representing text using numbers and building a chatbot.
⦁ Chapter 5, Word Embeddings and Distance Measurements for Text, will introduce you to wordlevel semantic embedding creation and establishing the similarity between documents.
⦁ Chapter 6, Exploring Sentence-, Document-, and Character-Level Embeddings, will dive deeper into techniques for embedding creation at character, sentence, and document level, along with building a spellchecker.
⦁ Chapter 7, Identifying Patterns in Text Using Machine Learning, will use machine learning algorithms to build a sentiment analyzer.
⦁ Chapter 8, From Human Neurons to Artificial Neurons for Understanding Text, will introduce you to the concepts of deep learning and how they are used for NLP tasks such as question classification.
⦁ Chapter 9, Applying Convolutions to Text, will discuss how convolutions can be used to extract patterns in text data for solving NLP problems such as sarcasm detection.
⦁ Chapter 10, Capturing Temporal Relationships in Text, will explain how to extract sequential relationships prevalent in text data and build a text generator using them.
⦁ Chapter 11, State of the Art in NLP, will discuss recent concepts, including Seq2Seq modeling, attention, transformers, BERT, and will also see us building a language translator.
