As the importance of machine learning and artificial intelligence accelerates, natural language processing takes a major function in filling the gap between the communication of humans and computers or machines. Though NLP is a recently trending branch of AI, there are already enough videos and tutorials available on the Internet including Natural Language Processing Books, Audio and PDF’s. Books can be often considered as a great start for beginners to learn a new technology or skill as a result we have listed some great books available for learning NLP by covering it’s major technical concepts and real-life examples.
Let’s Get Started With The List Of Natural Language Processing Books(NLP) –
This book provides a comprehensive point-based description of building real-world NLP applications. It will guide you through the process of developing Authentic NLP solutions placed and used in a larger product system. This guide is practical for people who want to develop, manage, and scale NLP systems in a major business environment and modify them for niche industries. You will also learn how to adapt to the various industry verticals such as healthcare, Media, and retail, etc. At the end of this book, you will Understand the wide range of problems, tasks, and different approaches within NLP. Also, you will learn to Perfectly Implement and evaluate different NLP applications machine learning methods. Evaluate all the different algorithms and approaches for NLP product tasks and datasets. Develop and create software solutions by maintaining the best practices during release, deployment, and DevOps for Natural Language Processing systems.
This book Natural Language Processing in Action is your guide to developing machines that can read and interpret the language used by humans. Within this, you’ll use obtainable Python packages to encapsulate the understanding in text and react to it. This book extends conventional NLP ways to include neural networks, modern deep learning algorithms, and major generative techniques when you tackle real-world issues like extracting names, places and dates, text composing, and free-form answering of listed questionnaires. You will just need a basic understanding of deep learning and intermediate-level Python knowledge. This book also shows how to work with Keras, TensorFlow, Gensim, and SCIkit including scalable pipelines and rule-based NLP. So get this book and Learn all the theory and practical skills necessary to go further by merely understanding the inner functioning of NLP, and start developing your own algorithms and models.
It is one of the most sought after and famously referenced and recommended books in the field of NLP. It is authored by Professor Dan Jurafsky from Stanford and James Martin from the University of Colorado. It will give you a deep understanding of the subject of natural language processing. It’s focused on supporting undergraduate or master’s courses in NLP or Computational Linguistics. It is considered as a must-read for those who are diving into the theory and it’s application part of language processing as they rise towards acquiring the skills necessary to strengthen their analytics abilities. A deep focus on web-based language approach, distinct field merging phone-based systems for dialogues, etc. Practical applications are emphasized with technical evaluation. This second edition is much more goal-oriented and can be considered as an extended version of the previous book.
This book gives a technical direction on the subject of natural language processing―the different ways for developing computer software that understands, produces, and manipulates the language of humans. It emphasizes recent data-driven approaches, attentive towards the techniques from supervised and non-supervised machine learning. Starting with the first section of the book that creates a basic ideation into machine learning by developing a set of tools that can be used throughout the book and application of those for practical word-based textual analysis. This book provides a superb introduction to natural language processing, with the main emphasis on foundational method building and algorithms.
This book is a classic material on this subject of NLP. This is a revision of the original book that offers a comprehensive introductory information to natural language understanding with the latest research and developments in the field today. When compared with the first edition of superior understanding and subjective foundation, the new edition provides the learners and readers with the same kind of balanced description of syntax, semantics, and discourse, and concentrates on a uniform framework determined on feature-based context-free grammar and chart Parsers utilized for semantic and syntactic processing. Extensive treatment of problems and issues in discourse and context-dependent explanation is also given.
This foundational book is the first detailed oriented introductory book of statistical natural language processing (NLP) to occur in the market. This book has all the theory and algorithms required for developing NLP tools. Statistical natural-language processing is, one of the most fast-moving and exciting branches of computer science nowadays. Learners or Practitioners and students who want to know this field would be advised to buy this book. It can be called as the most well-thumbed books to be considered for this topic. It gives a broad and deep coverage of mathematical and linguistic basics, as well as in-detail discussions of statistical methods, allowing all the students, learners, and researchers to construct their implementations. The book details the concepts of collocation findings, probabilistic parsing, data retrieval, and other applications.
This book is authored by Yoav Goldberg and can be considered only as an Introductory textbook. Neural networks are known as a family of powerful machine learning models. This book sole emphasis is on the application of neural network models to natural language data. The first 50 percent of the book covers all the basics of supervised machine learning and feed-forward neural networks, the fundamentals of working with machine learning over language-based data, and the usage of vector-based data rather than symbolic rendering for words. It also focuses on the computation-graph abstraction, that allows to define and train arbitrary neural networks with ease and is the important basis behind the design of modern neural network software libraries.
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