Best Natural Language Processing Books 2021 for Beginners

Best Natural language processing books
Natural language processing means the ability of a computer program to understand human language as it's spoken. In this modern era of technology, the use of world wide web and social media is increasing drastically and the amount of data is also increasing for this day by, The mechanism to process this unstructured data and extract meaningful information from it becomes more complex. This complexity can be overcome only by using natural language processing. If you are interested enough in this more essential field of the modern world, below are given some of the best natural language processing books in 2021.
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
Author: Delip Rao,Brian McMahan
Published at: 11/02/2019
ISBN: 1491978236

Natural Language Processing with PyTorch provides you boundless opportunities for solving problems in artificial intelligence. If you are a developer or data analyzer and want to learn NLP, then this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. A solid grounding in NLP and deep learning algorithms are provided for you. Also, you can learn how to use PyTorch to build applications with rich data representations.  Each chapter includes several code examples and illustrations.

What you'll learn-

  • Computational graphs and paradigm
  • Basics of PyTorch optimized tensor manipulation library
  • Overview of traditional NLP concepts
  • Basic ideas for building a neural network
  • How to represent words, sentences, documents, and other features
  • How to generate sequence to sequence models
  • Details on designing patterns for building production NLP system

Natural Language Processing in Action: Understanding, analyzing, and generating text with Python
Author: Hobson Lane,Hannes Hapke,Cole Howard
Published at: 14/04/2019
ISBN: 1617294632

This Natural Language Processing in Action guide helps you to build your machines that can read and interpret human languages. It has python packages to capture the meaning in the text and react accordingly. Also, this Natural Language Processing book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.

What you'll learn-

  • Details on Keras, tensor flow, Gensim, and Scikit
  • Data based NLP
  • Baby steps with neural science
  • Loopy neural networks
  • Long short-term memory networks
  • Sequence to sequence model
  • Information extraction and answering question

Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras
Author: Bhargav Srinivasa-Desikan
Published at: 29/06/2018
ISBN: 178883853X

Natural Language Processing and Computational Linguistic show you the uses of natural language processing and computational algorithms. Its algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now with Python and tools like Gensim and spaCy. You can learn how to perform computational linguistics from very first concepts. From this, you can ready yourself to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. 

What you'll learn-

  • Text analysis in the modern age
  • NLP terminology and python tools and datasets
  • Conversion of textual data into vector space
  • NLP models for computational linguistics
  • Statistical learning and topic modeling
  • Deep learning techniques for text analysis using Keras
  • POS-Tagging and its Applications
  • NER-Tagging and its Applications
  • Dependency Parsing
  • Advanced Topic Modelling
  • Clustering and Classifying Text

Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies)
Author: Yoav Goldberg
Published at: 17/04/2017
ISBN: 1627052984

Neural Network Methods in Natural Language Processing helps you to learn the application of neural network and the network model of natural language. The first portion of this Natural Language Processing book covers the basics of machine learning than follow up its explanations on neural networks. Also, you can easily learn the basics of working over language data and vector-based symbolic representations. It provides a computational graph for allowing easy definition on arbitrary neural network.

What you'll learn-

  • Neural network architectures
  • 1D convolution neural network
  • Generation models
  • Algorithms for machine learning
  • Syntactic Parsing
  • Tree-shaped network
  • Structured prediction
  • Design methods for contemporary neural networks

Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
Author: Steven Bird,Ewan Klein,Edward Loper
Published at: 10/07/2009
ISBN: 0596516495

Natural Language Processing with Python offers you a highly accessible fundamental of natural language processing. It gives you the varieties of language technologies and email filtering. You can learn how to write python programs that will work on a large amount of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures. Also, it gives you to understand the main algorithms for analyzing the content and structure of written communication. Overall, this book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open-source library.

What you'll learn-

  • Extraction of data from unstructured text
  • Topic identifier
  • The linguistic structure in text
  • Parsing and semantic analysis
  • Popular linguistic databases
  • Wordnet and trees
  • Basics on artificial intelligence

Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Author: Thushan Ganegedara
Published at: 31/05/2018
ISBN: 1788478312

This Natural Language Processing with TensorFlow book supplies the majority of data on deep learning applications. It brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data. You can learn how to use word2vec, advanced extensions, and word embeddings. All the chapters basically on the convolution of neural networks, recurrent of neural networks. Also, it will give you high-performance RNN models, LSTM and NLP tasks. You will also explore neural machine translation and implement a neural machine translator. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

What you'll learn-

  • Natural language processing using Tensorflow
  • Tensor flow tools and deep learning approaches
  • Process and evaluate large unstructured text datasheets
  • How to solve NLP tasks
  • Language generation using CNNs and RNNs
  • Advanced RNNs and long short-term memory
  • How to write automatic translation programs and implement an actual neural machine translator from scratch
  • Strategies to process large amounts of data into word representations 

Deep Learning for Natural Language Processing: Creating Neural Networks with Python
Author: Palash Goyal
Published at: 27/06/2018
ISBN: 148423684X

This excellent Deep Learning for Natural Language Processing will be a good starting point for people who want to get started in deep learning for NLP. All the codes available here will help you to try out the examples and extend them in interesting ways. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools like Keras. You can discover the concepts of deep learning from this full-filed amazing book.
What you'll learn-

  • Fundamentals of deep learning
  • Mathematical prerequisites
  • How to develop a chatbot
  • Implementation of research paper on sentiment classifications
  • Sequence to sequence models
  • Basics on neural network 
  • Examples on neural network
  • Long short-time memory network

Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications
Author: Kedia, Aman
Published at: 26/06/2020
ISBN: 1838989595

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.

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.

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