2. COURSE OVERVIEW ADVANCED TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics

3. Week 1 2  Probability Part 1  Questions we will answer:  What is probability?  What do we mean by random?

4. Week 2 3  Probability Part 2  Questions we will answer:  How can we leverage probability theory to help us?  What types of probabilities are important to us?

5. Week 3 4  Information Theory Overview  Questions we will answer:  What is information?  What is information theory?  What is entropy?

6. Week 4 5  Computational Linguistics Overview  Questions we will answer:  What are parts of speech?  What is morphology?  What is a phrase?  What are semantics?  What are pragmatics?

7. Week 5 6  Text Categorization Overview  Questions we will answer:  What is text categorization?  What resources are necessary to categorize text?  What is a controlled vocabulary?

8. Week 6 7  Text Categorization Implementation  Questions we will answer:  What machine learning models perform categorization?  How can I use a machine learning model to categorize text?

9. Week 7 8  Text Clustering Overview  Questions we will answer:  What is text clustering?  What algorithms can I use to cluster text?

10. Week 8 9  Text Clustering Implementation  Questions we will answer:  How can I use a clustering algorithm to cluster text?

11. Textbooks 10  The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data  by Ronen Feldman, James Sanger  Publisher: Cambridge University Press (December 11, 2006)  ISBN-13: 978- 0521836579  NLTK ― Natural Language Processing with Python ― Analyzing Text with the Natural Language Toolkit  by Steven Bird, Ewan Klein, and Edward Loper  Publisher: O'Reilly  ISBN-13: 9780596516499.  The book is online at: (http://nltk.org/book/)http://nltk.org/book/

12. The end has come. End of Overview Slides 11