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Mastering NLP from Foundations to LLMs

You're reading from  Mastering NLP from Foundations to LLMs

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Pages 340 pages
Edition 1st Edition
Languages
Authors (2):
Lior Gazit Lior Gazit
Profile icon Lior Gazit
Meysam Ghaffari Meysam Ghaffari
Profile icon Meysam Ghaffari
View More author details

Table of Contents (14) Chapters

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

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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