Top 7 Free NLP Books To Read

Natural Language Programming or NLP has enabled computers to interpret human language that has further opened doors to new innovation. Due to this very reason, the interest to learn more about the subject has increased in recent years, and as they say, books are the best place to gather knowledge from. But when it comes to books, the options are in millions, and it is hard to zero-in on one. Also, people often tend to look for the ones freely available in the form of eBook or PDFs, which are quite hard to come by.

In this article, we present to you the top 7 NLP books one can get their hands on for free.

Natural Language Processing with Python

Authored by Steven Bird, Ewan Klein and Edward Loper, this book is found in the top of every NLP book list, and so we could not discard it either. The book focuses on analysing text with natural language toolkit. The book covers chapters such as processing raw text, writing structured programs, learning to classify text and analysing sentence structure to point out a few. The book is mostly aimed to cater to beginners in NLP, computational linguistics and AI developers. The best aspect of this book is that it does not throw complex theories at a reader. Rather, it gives plenty of code and concepts to start experimenting immediately.

Find it here for free.

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Penned by Daniel Jurafsky and James H. Martin, the book is meant for beginners natural language and speech processing as it introduces a reader to linguistics and computer science.

The books work with several examples of applying statistics and other machine learning algorithms. Some of the highlighting topics the books covers are symbolic approaches to language processing, information extraction, machine translation and creation of spoken-language dialogue agents. Some notable chapters to be highlighted here include N-gram language models, WordSense and WordNet, and constituency grammars.

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The Oxford Handbook of Computational Linguistics

Edited by Ruslan Mitkov, the book consists of 38 chapters that cover major concepts, methods and applications in computational linguistics. Each and every chapter in the book is put together by experts in the field from different corners of the world. The book is aimed at those who are post-doctoral students or researchers in the field of informatics, artificial intelligence, language engineering, and cognitive science. Some of the notable chapters from the book are phonology, morphology, syntax, text segmentation, parsing, word-sense disambiguation, lexical knowledge acquisition and corpus linguistics, to name a few.

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Text Mining with R

The book is aimed to cater to those practitioners who are slightly familiar with R. Penned down by Julia Silge and David Robinson, the book covers statistical natural language processing methods on a modern range of applications. Some of the chapters listed here are tidy text format, Sentiment analysis with tidy data, converting to and from non-tidy formats and topic modelling. 

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Taming Text

Authored by Grant S. Ingersoll, Thomas S. Morton and Andrew L. Fariss, the book provides an example-driven guide to work with unstructured text. It teaches about how to automatically organise text using approaches such as full-text search, proper name recognition and clustering. It also takes a reader through open-source libraries like Solr and Mahout along with the ways to build text-processing applications. A few chapters that can be pointed are fuzzy string matching and untamed text.

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st Edition)

Written by Aurélien Géron, this is a practical book that takes a reader through the way to use concrete examples, minimal theory and two production-ready Python frame. The book is not dedicated to natural programming languages, but it showcases popular libraries that one can put to use for NLA and text analysis.

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Natural Language Processing Succinctly

Penned by Joseph D. Booth, the book describes modern NLP that is based on machine learning, which tries to improve software for recognising patterns, using context to infer meaning and accurately discern poorly structured text. A few highlighting chapters to be mentioned are tagging, extracting sentences, extracting words, entity recognition, Cloudmersive and Google Cloud NLP API.

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