When it comes for choosing a book then we are always confused o what should we choose? What will be the better guide for your learning? Will the beginners guide be too easy? So here are few books which will help you find the leading learning guide for your better career.
1.Natural language processing with Python
By Steven Bird, Ewan Klein
It is so popular, that every top seems to have it listed. Well, it is a timeless classic that provides an introduction to NLP using the Python and its NLTK library. The once who are the beginners in NLP, computational linguists and the developers of artificial intelligence. This book is good as it is very practice oriented and here you will not be introduced to the theories which are complex and just plenty of codes and concepts will not be there for experimenting the right way.
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2. Foundations of Statistical Natural Language Processing
By Christopher Manning
This book offers a thorough introduction to statistical methods for NLP and it covers both the linguistic essentials and basic statistical methods as of 1999.Beginners in natural language processing having no required knowledge of linguistics or statistics can use this book.
Though this book is old but, this book gives a strong foundation in linguistics and statistical methods and to better understand the newer methods and encodings.
3. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
by Dan Jurafsky and James H Martin
This book is quite old and also offers a simplified vision of speech and the processing language which covers the statistical and symbolic approaches for the processing of the language and it also presents the algorithm and the speech recognition, correction of grammar and spelling, search engines and creation of spoken language dialog agents. Beginners in natural language and speech processing can avail this book. The book provides a solid foundational knowledge as it introduces linguistics, computer science and statistics at comprehensive depth.
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4. The Oxford Handbook of Computational Linguistics
By Ruslan Mitkov
This handbook describes major concepts, methods, and applications in computational linguistics, starting from linguistic fundamentals comprehensible even for undergraduates and non-specialists from other fields of linguistics and proceeding with overview of current tasks, techniques, and tools in Natural Language Processing targeting more experienced computational language researchers. Linguists as well as researchers in informatics, artificial intelligence, language engineering, and cognitive science can read this book. This book is an academic edition, meaning that it theory-oriented and provides deeper understanding of major concepts that their functioning.
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5. Text Mining with R
By Julia Silge and David Robinson
This book presents an introduction of text mining using the tidy text package and other tidy tools in R. It demonstrates statistical natural language processing methods on a range of modern applications.
Practitioners at least slightly familiar with R can go through it. It is good as is quite new; therefore it has a practical and modern feel to the demonstrations and provides examples of real text mining problems.
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6. Taming Text
by Grant Ingersoll, Thomas Morton and Drew Farris.
This book provides an introduction to several NLP tools and problems, including Apache Sole, Apache Open NLP, and Apache Mahout with code samples in Java.Software developers who want to familiarize themselves with enterprise-grade NLP tools for work projects should go through this book.
This book offers first-hand insights into Apache-based NLP a co founder of the Apache Mahout project. Besides, it is a rare book having Java code examples.
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