This document provides an overview of natural language processing (NLP) for text categorization and classification. It discusses supervised and unsupervised learning problems and classification algorithms like Naive Bayes and support vector machines (SVM). Specific applications mentioned include email classification, spam filtering, and document organization. The document compares Naive Bayes and SVM, noting that Naive Bayes is easier and faster while SVM is more difficult but can handle binary classification problems.

1. NLP For
Text Categorization/Classification
Domain-Natural Language Processing
Prepared By-Abhishek Oswal
Guide-Jayshree Ghorpade

2. Some Questions
What is NLP Detecting ->Patterns
Features Models
What is Cassification
What is Text Classification
Why Text Classification

3. Promblem Type
•Supervised
•You know about it
•Train data
•Fruits Analogy
•Unsupervised
•You don't know about it
•Untrain data

4. Supervised
•Regression and Classification
•Regression -> Real estate market predict
price ,
•Price Continious Output
•Classification ->Whether it sells for more or
less than asked price,discrete output

5. Process of Classification
•Data preprocessing
•Training and Test set
•Creation of model
•Algorithm
•Classify

6. Methods To Represent
•Document -term Matrix
•Bags of words

7. Methods To Classify
•Using Probability
•Naive Bayes
•Using Graphs
•Simple Vector Machine
•Tree
•Decision Tree

8. Naive Bayes
•Predictice Model
•Conditional Probability

9. Naive Bayes
•Independent Features
•Prior
•Likelihood

10. Naive Bayes

11. Naive Bayes

12. Another Method

13. Simple Vector Machine

14. SVM
lOptimal plane

15. SVM
•Using margin
•Marging is no man's land

16. SVM
•Optimal plane would be one with Biggest
margin
•Equation of hyperplane

17. Applications
•Email Classification
•Spam Filtering
•News Organization
•Classification of documents based on language
•Opining Mining
•Eg.
•Sakaal Classifieds
•Gmail Spam Mail Detection

18. Example

19. Comparision
Naive Bayes
Easy
Fast
Different Classes
SVM
Difficult
Slow (traning time)
Binary Output

20. Questions
• My Questions First
•Classify my presentation
•Class ->Good /Bad

21. Fei Yu ,Jiyao An and Hong Li,Mialiang Zhu and
Ouyang Yang,”Intelligence Text Categorization
Based on Bayes Algorithm”,Proceedings of
International Conference on Information
Acquisition.