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)

NER

NER is an NLP technique that’s designed to detect and categorize named entities within text, including but not limited to person’s names, organization’s names, locations, and more. NER’s primary objective is to autonomously identify and extract information about these named entities from unstructured text data.

NER typically involves using machine learning models, such as conditional random fields (CRFs) or recurrent neural networks (RNNs), to tag words in a given sentence with their corresponding entity types. The models are trained on large annotated datasets that contain text with labeled entities. These models then use context-based rules to identify named entities in new text.

There are several categories of named entities that can be identified by NER, including the following:

  • Person: A named individual, such as “Barack Obama”
  • Organization: A named company, institution, or organization, such as “Google”...