Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)

Removing special characters and punctuation

Removing special characters and punctuation is an important step in text preprocessing. Special characters and punctuation marks do not add much meaning to the text and can cause issues for machine learning models if they are not removed. One way to perform this task is by using regular expressions, such as the following:

re.sub(r"[^a-zA-Z0-9]+", "", string)

This will remove non-characters and numbers from our input string. Sometimes, there may be special characters that we would want to replace with a whitespace. Take a look at the following examples:

  • president-elect
  • body-type

In these two examples, we would want to replace the “-” with whitespace, as follows:

  • President elect
  • Body type

Next, we’ll cover stop word removal.

Stop word removal

Stop words are words that do not contribute much to the meaning of a sentence or piece of text, and therefore can...