Best Natural Language Processing Books 2023 for Beginners

What you will learn

• Become well-versed with basic and advanced NLP techniques in Python
• Represent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddings
• Perform text classification using different methods, including SVMs and LSTMs
• Explore different techniques for topic modellings such as K-means, LDA, NMF, and BERT
• Work with visualization techniques such as NER and word clouds for different NLP tools
• Build a basic chatbot using NLTK and Rasa
• Extract information from text using regular expression techniques and statistical and deep learning tools

With a basic understanding of machine learning and some Python experience, you’ll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP.

• Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension
• Train NLP models with performance comparable or superior to that of out-of-the-box systems
• Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm
• Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai
• Build core parts of the NLP pipeline–including tokenizers, embeddings, and language models–from scratch using Python and PyTorch
• Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

What you will learn

• Use the latest pre-trained transformer models
• Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
• Create language understanding Python programs using concepts that outperform classical deep learning models
• Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
• Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
• Measure the productivity of key transformers to define their scope, potential, and limits in production

With this book, you’ll learn:

• Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP
• Implement and evaluate different NLP applications using machine learning and deep learning methods
• Fine-tune your NLP solution based on your business problem and industry vertical
• Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages
• Produce software solutions following best practices around the release, deployment, and DevOps for NLP systems
• Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

What you will learn

• Understand the transformer model from the ground up
• Find out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasks
• Get hands-on with BERT by learning to generate contextual word and sentence embeddings
• Fine-tune BERT for downstream tasks
• Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models
• Get the hang of the BERT models based on knowledge distillation
• Understand cross-lingual models such as XLM and XLM-R
• Explore Sentence-BERT, VideoBERT, and BART

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 modelling
• Discover techniques to represent text as word and document vectors

Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You’ll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You’ll even learn how to transform statements into questions to keep a conversation going.

You’ll also learn how to:   

• Work with word vectors to mathematically find words with similar meanings (Chapter 5)   
• Identify patterns within data using spaCy’s built-in displaCy visualizer (Chapter 7)   
• Automatically extract keywords from user input and store them in a relational database (Chapter 9)  
• Deploy a chatbot app to interact with users over the internet (Chapter 11)

“Try This” sections in each chapter encourage you to practice what you’ve learned by expanding the book’s example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications.

By the end of the book, you’ll be creating your own NLP applications with Python and spaCy.

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.

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

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 Processingbook 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 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 Processinghelps 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 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

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

• 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

This textbook provides a technical perspective on natural language processing―methods for building computer software that understands, generates and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to represent and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation.

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems.

Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods.

Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for upper-undergraduate and graduate students.