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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: 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)
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POS tagging

POS tagging is the practice of attributing grammatical labels, such as nouns, verbs, adjectives, and others, to individual words within a sentence. This tagging process holds significance as a foundational step in various NLP tasks, including text classification, sentiment analysis, and machine translation.

POS tagging can be performed using different approaches such as rule-based methods, statistical methods, and deep learning-based methods. In this section, we’ll provide a brief overview of each approach.

Rule-based methods

Rule-based methods for POS tagging involve defining a set of rules or patterns that can be used to automatically tag words in a text with their corresponding parts of speech, such as nouns, verbs, adjectives, and so on.

The process involves defining a set of rules or patterns for identifying the different parts of speech in a sentence. For example, a rule may state that any word ending in “-ing” is a gerund (a verb...

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