Top Natural Language Processing (NLP) Books

It is important to understand both theoretical foundations and practical applications when it comes to NLP. There are many books available that cover all the key concepts, methods, and tools you need. Whether you are a beginner or a professional, choosing the right book can be challenging.

In this article, we will look at some Top NLP books, Why they are unique, and why you should read them.

Top 7 Natura Language Books(NLP) Books

Natural Language Processing (NLP) is a booming field and an integral part of artificial intelligence and machine learning. It is dedicated to interaction between the computers and human languages. Thus, NLP enables machines to understand, interpret, and generate human language in a valuable way. The best book on NLP would be the one covering all the foundational concepts, core techniques and methodologies (like text preprocessing and statistical models), practical applications, and tools. It should have advanced topics that cover the recent advancement in the field and consideration of ethics and bias. Further, Hands-On Projects and problem sets can perfectly help to practice what you learn.

You can refer to the following books to understand the theory, methodology, and tools that are used in this dynamic field. The list given below doesn't indicate a ranked preference. However, an insight is provided in detail on each books content so that you can choose a book as per your needs.

1. "Practical Natural Language Processing"

This book presents a complete look on constructing real world NLP applications. it covers the whole lifecycle of a typical NLP project - right from data gathering to installing and monitoring the model. While some of these processes are highly particular to NLP, others can be used to any ML pipeline. In order to create an NLP system from scratch, the book also offers task-specific case studies and domain-specific instructions.

Why should one learn from this book?

• It covers Text representation
• Most common NLP tasks such as classification, entity recognition, knowledge dissemination
• Tasks which involve cross engineering expertise such as social media mining etc.
• Explicable AI where they show how-to explain the decision of a classifier; working with limited data.

Authors: Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana

2. "Speech and Language Processing"

This book presents cutting-edge algorithms and methods for text-based and speech-based natural language processing, providing a cohesive view of speech and language processing. It discusses both statistical and symbolic approaches to language processing and demonstrates how they can be used for crucial tasks like machine translation, speech recognition, information extraction, search engines, spelling and grammar correction, and the development of spoken-language dialog agents.

Why should one learn from this book?

• Covers language models and Language Model , Part-of-Speech tagging
• Explains Hidden Markov Model (HMM) and Context-Free Grammar
• Dicusses about Probabilistic Context-Free Grammar and Discourse analysis
• Provides Dialogue System Machine Translation

Authors: Dan Jurafsky and James H. Martin

3. "Foundations of Statistical Natural Language Processing"

The book contains all the theory and algorithms that you will ever need for building NLP tools. It broadly covers mathematical and linguistic foundations and also has statistical methods discussed in details. Thus, this book allows the students and researcher to construct their own implementations.

Why should one learn from this book?

• Covers collocation finding and word sense disambiguation
• Explains Probabilistic parsing , information retrieval, and other applications.

Authors: Christopher D. Manning and Hinrich Schütze

4. "Neural Network Methods in Natural Language Processing"

This book focuses on using neural network models to analyze natural language data. The style of writing in the book invites you to consider the reasons behind things happening and whether you can apply these networks to address specific issues in your own life. Being able to reason about the newest and finest tools is quite helpful, as NLP is still pretty challenging (relative to the field day computer vision has been having).

Why should one learn from this book?

• Principles of supervised machine learning
• Feed-forward neural networks
• Fundamentals of dealing with machine learning over language data
• Usage of vector-based rather than symbolic representations for words

Authors: Yoav Goldberg

5. "Natural Language Understanding"

Natural Language Understanding provides a far better introduction to NLP/AI than Speech and Language Processing (2nd Edition). Even though some jargon is inevitable, NLU uses very little of it and makes it easy to read.

Why should one learn from this book?

• Statistically-based methods using large corpora
• Speech recognition and spoken language understanding
• Information on semantics

Authors: James Allen

6. "Natural Language Processing with Python"

The book "Natural Language Processing USING Python" will show you how to process natural language. If you're interested in learning more about natural language processing, it's best to consult their documentation if you're already proficient in the field.

For instance, this book will explain tokenization to you, but it won't provide you with a list of all the tokenizers on NLTK (for example, TweetTokenizer is only applicable to tweets; you can learn more about it from the documentation). Keep in mind that without understanding what natural language processing is, you cannot start with the documentation of NLTK.

Why should one learn from this book?

• Identify "named entities" or infer the topic from unstructured text by extracting information about it.
• Examine the text's linguistic structure using parsing and semantic analysis.
• Use well-known language databases, such as WordNet and treebanks.
• Combine methods from a variety of disciplines, including artificial intelligence and linguistics.

Authors: Steven Bird, Ewan Klein, and Edward Loper

7. "Natural Language Processing in Action"

"Natural Language Processing in Action" will help you develop machines that can read and interpret human language. In it, you will use easily accessible Python libraries to extract text's meaning and respond appropriately. As you go through real-world issues like extracting names and dates, creating text, and responding to open-ended queries, the book broadens the scope of traditional natural language processing (NLP) approaches to encompass neural networks, contemporary deep learning algorithms, and generative techniques. This book requires basic understanding of deep learning and intermediate Python skills.

Why should one learn from this book?

• This book has some NLP-written sentences! Can you figure out which ones?
• Working with gensim, scikit-learn, TensorFlow, and Keras
• Data-driven and rule-based natural language processing
• Scalable pipelines

Authors: Hobson Lane, Cole Howard, and Hannes Hapke

Additional NLP Books to Consider

Apart from these Top NLP books , You can slo recommend below mentioned books to learn Natural Language Processing (NLP).

10. "Statistical Methods for Speech Recognition"

This book focuses on statistical models used in speech recognition, an important aspect of NLP.

Why should one learn from this book?

• Detailed coverage of statistical modeling techniques
• Applications in speech recognition and language processing

Author: Frederick Jelinek

11. "Natural Language Annotation for Machine Learning"

This book provides guidance on annotating data for machine learning models, crucial for training NLP systems.

Why should one learn from this book?

• Practical advice on creating high-quality annotated data
• Techniques for different types of annotations and tasks

Authors: James Pustejovsky and Amber Stubbs

12. "Natural Language Processing with Java"

This book introduces NLP using Java, offering an alternative for those who prefer Java over Python.

Why should one learn from this book?

• Covers core NLP tasks and techniques using Java
• Practical examples and projects to implement NLP solutions

Authors: Richard M. Reese and Ashish Singh Bhatia

Conclusion

Thus, we have discussed seven books on NLP that you can read straightaway to whet your skills and create some cool applications based on NLP. There are many other books that you may explore, however, we have mentioned the books that are most popular and preferred by the industry experts for their reference. You may choose any of the books by assessing depth of your current knowledge in NLP and what interests you the most.