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Before the hype around large language models (LLMs), NLP was building but was progressing in the lurk. Now it has become revolutionized since the release of LLMs such as ChatGPT. LLMs have been shown to understand as well as generate human-like text. Models such as ChatGPT, Google Bard, and more have been trained on high volumes of text data within a deep neural network architecture. 

But how do these models understand humans exactly, as well as output human-like reponses? NLP. A subfield of artificial intelligence that helps models process, understand and output the human language. They are typically trained on tasks such as next word prediction which allow them to build contextual dependencies and then be able to generate relevant outputs. The NLP field has advanced applications such as chatbots, text summarization, and more. 

There are some ethical concerns around LLMs and their bias in text generation, sparking further research into NLP and its use in LLM applications. Although these concerns and challenges are currently being addressed, with the impact LLM models such as ChatGPT have had on the world - it looks like they’re here to stay and understanding NLP will be essential. 

If you want to understand more about LLMs, you need to learn about NLP. In this article, I will go through 5 FREE books which you need to read in 2023 to get a better grasp of NLP. 

1. Speech and Language Processing

Authors: Dan Jurafsky and James H. Martin

Link: Speech and Language Processing

Written by two university professors, this Speech and Language Processing book provides you with a comprehensive introduction to the world of NLP. It is broken down into 3 sections: Fundamental Algorithms for NLP, NLP Applications, and Annotating Linguistic Structure. The first section is essential to beginners to get a better understanding of what NLP is, the foundations of it with examples breaking it down. You will come across a range of topics such as semantics, syntax, and more. 

If the field of NLP is new to you or you want to transition into the field, I truly believe this book will be very beneficial to an individual's learning. As it was written by professors, the practical examples help readers understand the concepts much better than a purely theoretical book. 

2. Foundations of Statistical Natural Language Processing 

Authors: Christopher D. Manning and Hinrich Schütze

Link: Foundations of Statistical Natural Language Processing

If you are a data professional, or in the world of artificial intelligence - you will know how important statistics is to the field. Some believe that you do not require a high understanding of the sector, however I believe it is important as it will make your data professional journey much smoother. 

When you have a good foundation about the NLP field, you might think the next step is to learn about the algorithms. Before that, you will want to learn more about the mathematical foundations of language. This book not only starts with the basics of NLP, it dives into the mathematical aspects such as probability spaces, bayes’ theorem, variance, and more. 

3. Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Link: Pattern Recognition and Machine Learning

The best way to understand the performance of models is by understanding how the model works, its train of thought, pattern recognition and why it outputs what it does. Pattern recognition is the process of distinguishing data based on a set criteria performed by special algorithms.It enables learning and allows for room for improvement, which makes it very important to machine learning algorithms and their performance. 

Every chapter has an exercise at the end which has been chosen to better explain each concept to the reader. The author kept the mathematical content at a minimum to help the reader grasp a better understanding, however it is noted that it will be beneficial to have a good grasp of calculus, linear algebra, and probability theory to understand pattern recognition and machine learning techniques. 

4. Neural Network Methods in Natural Language Processing 

Author: Yoav Goldberg

Link: Neural Network Methods in NLP

When looking into the growth of NLP, we can say that neural networks have played a big part. Neural networks have provided NLP models with a better understanding of the human language, allowing them to predict words and compartmentalize different topics that were not previewed to them during their learning face. 

This book does not dive into the ins and outs of neural networks straight away. It starts off with learning the basics such as linear models, perceptrons, feed-forward, neural network training and more. The author has used a mathematical approach to explain these fundamental elements along with practical examples.

5. Practical Natural Language Processing 

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

Link: Practical Natural Language Processing 

So you’ve understood speech and language, you’ve covered statistical NLP, then looked at pattern recognition and neural networks in NLP. The last thing you need to learn about is the practical use of NLP. 

This book goes through how NLP is used in the real world, the pipeline of NLP models, and more about text data and use cases, such as Chatbots like ChatGPT. In this book you will learn how NLP can be used in a variety of sectors such as retail, healthcare, finance, and more. With the different sectors, you will be able to gauge how the NLP pipeline works for each, and be able to figure out how to use it for yourself. 

Wrapping it up

The aim and flow of this article was to provide you with 5 free books which I believe are essential and will benefit your NLP career or study. Although I did it in a structure format, I hope each book bounces off the other taking your studying to the next level.

If there are any other FREE NLP books which you believe others would benefit from, please drop them in the comments!
 
 
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.