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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms Popular algorithms for data science and machine learning

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789347999
Length 522 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (19) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Regression Algorithms 5. Linear Classification Algorithms 6. Naive Bayes and Discriminant Analysis 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Advanced Clustering 11. Hierarchical Clustering 12. Introducing Recommendation Systems 13. Introducing Natural Language Processing 14. Topic Modeling and Sentiment Analysis in NLP 15. Introducing Neural Networks 16. Advanced Deep Learning Models 17. Creating a Machine Learning Architecture 18. Other Books You May Enjoy

Part-of-Speech

In some cases, it can be helpful to detect the single syntactical components of a text to perform specific analyses. For example, given a sentence, we can be interested in finding the verb that represents the intent of an action. Alternatively, we could need to extract other attributes such as locations, names, and temporal dependencies. Even though this topic is quite complex and beyond the scope of this book, we wanted to provide some examples that can be immediately applied to more complex scenarios.

The first step of this process is called POS Tagging and consists of adding a syntactic identifier to each token. NLTK has a built-in model based on the Penn Treebank POS corpus, which provides a large number of standard tags for the English language (for a complete list, please check out https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html...

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