1. Hands on Classification with Learning Based Java Gourab Kundu Adapted from a talk by Vivek Srikumar

2. Goals of this tutorial At the end of these lectures, you will be able to 1. Get started with Learning Based Java 2. Use a generic, black box text classifier for different applications …and write your own text classifier, if needed 3. Understand how features can impact the classifier performance … and add features to improve your application 4. Build a badge classifier based on character features

3. A Quick Recap Given: Examples (x,f(x)) of some unknown function f Find: A good approximation of f x provides some representation of the input  The process of mapping a domain element into a representation is called Feature Extraction. (Hard; ill-understood; important)  x €{0,1} n or x € R n The target function (label)  f(x) € {-1,+1} Binary Classification  f(x) € {1,2,3,.,k-1} Multi-class classification

4. What is text classification? ✓ ✗ ✗ ✗ A document Some labels A classifier (black box)

5. Several applications fit this framework Spam detection Sentiment classification What else can you do, if you had such a black box system that can classify text? Try to spend 30 seconds brainstorming

6. Outline of this session Getting started with LBJ Writing our first classifier: Spam/Ham Playing with features Looking inside the black box classifier for feature weights

7. LEARNING BASED JAVA Writing classifiers

8. What is Learning Based Java? A modeling language for learning and inference Supports  Programming using learned models  High level specification of features and constraints between classifiers  Inference with constraints  Different learning algorithms The learning operator  Classifiers are functions defined in terms of data  Learning happens at compile time

9. What does LBJ do for you? Abstracts away the feature representation, learning and inference Allows you to write learning based programs Application developers can reason about the application at hand

10. Demo A learning based program First, we will write an application that assumes the existence of a black box classifier

11. SPAM DETECTION

12. Spam detection Which of these (if any) are email spam? Subject: save over 70 % on name brand software ppharmacy devote fink tungstate brown lexicon pawnshop crescent railroad distaff cytosine barium cain application elegy donnelly hydrochloride common embargo shakespearean bassett trustee nucleolus chicano narbonne telltale tagging swirly lank delphinus bragging bravery cornea asiatic susanne Subject: please keep in touch just like to say that it has been great meeting and working with you all. i will be leaving enron effective july 5 th to do investment banking in hong kong. i will initially be based in new york and will be moving to hong kong after a few months. do contact me when you are in the vicinity. How do you know?

13. What do we need to build a classifier? 1. Annotated documents * 2. A feature representation of the documents 3. A learning algorithm * Here we are dealing with supervised learning

14. Our first LBJ program /** A learned text classifier; its definition comes from data. */ discrete TextClassifier(Document d) <- learn TextLabel using WordFeatures from new DocumentReader("data/spam/train") with SparseAveragedPerceptron { learningRate = 0.1 ; thickness = 3.5; } 5 rounds testFrom new DocumentReader("data/spam/test”) end Defines a classifier The object being classified The function being learned The feature representation The source of the training data The learning algorithm

15. Demo Let’s build a spam detector  How to train?  How do different learning algorithms perform? Does this choice matter much?

16. Features Our current spam detector uses words as features Can we do better? Let’s try it out

17. MORE TEXT CLASSIFICATION

18. Sentiment classification Which of these product reviews is positive? I recently made the switch from PC to Mac, and I can say that I'm not sure why I waited so long. Considering that I have only had my computer a few weeks I can't say much about the durability and longevity of the hardware, but I can say that the operating system (mine shipped with Lion) and software is top notch. I've been an Apple user for a long time, but my most recent MacBook Pro purchase has convinced me to reconsider. I've had several hardware issues, including a failed keyboard, battery failure, and a bad DVD drive. Now, the backlight on the display fails to turn on when waking from sleep How do you know?

19. Classifying news groups Which mailing list should this message be posted to? I am looking for Quick C or Microsoft C code for image decoding from file for VGA viewing and saving images from/to GIF, TIFF, PCX, or JPEG format. I have scoured the Internet, but its like trying to find a Dr. Seuss spell checker TSR. It must be out there, and there's no need to reinvent the wheel. How do you know? alt.atheism comp.graphics comp.os.ms-windows.misc comp.sys.ibm.pc.hardware comp.sys.mac.hardware comp.windows.x misc.forsale rec.autos rec.motorcycles rec.sport.baseball rec.sport.hockey sci.crypt sci.electronics sci.med sci.space soc.religion.christian talk.politics.guns talk.politics.mideast talk.politics.misc talk.religion.misc

20. Demo Converting our spam classifier into a  Sentiment classifier  A newsgroup classifier Note: How different are these at the implementation level?

21. Most of the engineering lies in the features ✓ ✗ ✗ ✗ A document Some labels A classifier (black box)

22. Summary What is LBJ? How do we use it? Writing a simple spam detector Playing with features How much do we need to change to move to a different application?

23. Assignment before Next Class (Not Graded) Download the code & data ( http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-12/handsonclassification.html ) for this class and play with it http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-12/handsonclassification.html Try to solve the Badges game puzzle with LBJ  Think about what features are needed  Write a parser for reading the data  Write a classifier for solving the puzzle

24. Next Class We will solve the Badges Game puzzle by Machine Learning We will look at more text classification examples We will think about a famous people classifier Questions

25. Badge Classifier Brainstorm the possible Features  Characters in entire name  Two consecutive Characters  Character as Vowel, Character as Consonant  ….  … Feature Engineering is Important (especially if labeled data is small) What is the baseline? 70 +, 24 -

26. THE FAMOUS PEOPLE CLASSIFIER

27. The Famous People Classifier f( ) = Politician f( ) = Athlete f( ) = Corporate Mogul

28. The NLP version of the fame classifier All sentences in the news, which the string Barack Obama occurs All sentences in the news, which the string Roger Federer occurs All sentences in the news, which the string Bill Gates occurs Represented by

29. Our goal Find famous athletes, corporate moguls and politicians Athlete Michael Schumacher Michael Jordan … Politician Bill Clinton George W. Bush … Corporate Mogul Warren Buffet Larry Ellison …

30. Let’s brainstorm How do we build a fame classifier? Remember, we start off with just raw text from a news website

31. One solution Let us label entities using features defined on mentions Identify mentions using the named entity recognizer Define features based on the words, parts of speech and dependency trees Train a classifier All sentences in the news, which the string Barack Obama occurs

32. Summary 1. Get started with Learning Based Java 2. Use a generic, black box text classifier for different applications …and write your own text classifier, if needed 3. Understand how features can impact the classifier performance … and add features to improve your application 4. Build a badge classifier based on character features Questions