1. Matwin 1999 1 Text classification: In Search of a Representation Stan Matwin School of Information Technology and Engineering University of Ottawa stan@site.uottawa.ca

2. Matwin 1999 2 Outline Supervised learning=classification ML/DM at U of O Classical approach Attempt at a linguistic representation N-grams – how to get them? Labelling and co-learning Next steps?…

3. Matwin 1999 3 Supervised learning (classification) Given: a set of training instances T={e t }, where each t is a class label : one of the classes C 1,…C k a concept with k classes C 1,…C k (but the definition of the concept is NOT known) Find: a description for each class which will perform well in determining (predicting) class membership for unseen instances

4. Matwin 1999 4 Classification Prevalent practice: examples are represented as vectors of values of attributes Theoretical wisdom, confirmed empirically: the more examples, the better predictive accuracy

5. Matwin 1999 5 ML/DM at U of O Learning from imbalanced classes: applications in remote sensing a relational, rather than propositional representation: learning the maintainability concept Learning in the presence of background knowledge. Bayesian belief networks and how to get them. Appl to distributed DB

6. Matwin 1999 6 Why text classification? Automatic file saving Internet filters Recommenders Information extraction …

7. Matwin 1999 7 Bag of words Text classification: standard approach 1.Remove stop words and markings 2. remaining words are all attributes 3.A document becomes a vector 4.Train a boolean classifier for each class 5.Evaluate the results on an unseen sample

8. Matwin 1999 8 Text classification: tools RIPPER A “covering”learner Works well with large sets of binary features Naïve Bayes Efficient (no search) Simple to program Gives “degree of belief”

9. Matwin 1999 9 “Prior art” Yang: best results using k-NN: 82.3% microaveraged accuracy Joachim’s results using Support Vector Machine + unlabelled data SVM insensitive to high dimensionality, sparseness of examples

10. Matwin 1999 10 SVM in Text classification SVM Transductive SVM Maximum separation Margin for test set Training with 17 examples in 10 most frequent categories gives test performance of 60% on 3000+ test cases available during training

11. Matwin 1999 11 Problem 1: aggressive feature selection

12. Matwin 1999 12 Problem 2: semantic relationships are missed

13. Matwin 1999 13 Proposed solution (Sam Scott) Get noun phrases and/or key phrases (Extractor) and add to the feature list Add hypernyms

14. Matwin 1999 14 Hypernyms - WordNet

15. Matwin 1999 15 Evaluation (Lewis) Vary the “loss ratio” parameter For each parameter value Learn a hypothesis for each class (binary classification) Micro-average the confusion matrices (add component-wise) Compute precision and recall Interpolate (or extrapolate) to find the point where micro- averaged precision and recall are equal

16. Matwin 1999 16 Results No gain over BW in alternative representations But… Comprehensibility…

17. Matwin 1999 17 Combining classifiers Comparable to best known results (Yang)

18. Matwin 1999 18 Other possibilities Using hypernyms with a small training set (avoids ambiguous words) Use Bayes+Ripper in a cascade scheme (Gama) Other representations:

19. Matwin 1999 19 Collocations Do not need to be noun phrases, just pairs of words possibly separated by stop words Only the well discriminating ones are chosen These are added to the bag of words, and… Ripper

20. Matwin 1999 20 N-grams N-grams are substrings of a given length Good results in Reuters [Mladenic, Grobelnik] with Bayes; we try RIPPER A different task: classifying text files Attachments Audio/video Coded From n-grams to relational features

21. Matwin 1999 21 How to get good n-grams? We use Ziv-Lempel for frequent substring detection (.gz!) abababa a b a a b b a

22. Matwin 1999 22 N-grams Counting Pruning: substring occurrence ratio < acceptance threshold Building relations: string A almost always precedes string B Feeding into relational learner (FOIL)

23. Matwin 1999 23 Using grammar induction (text files) Idea: detect patterns of substrings Patterns are regular languages Methods of automata induction: a recognizer for each class of files We use a modified version of RPNI2 [Dupont, Miclet]

24. Matwin 1999 24 What’s new… Work with marked up text (Word, Web) XML with semantic tags: mixed blessing for DM/TM Co-learning Text mining

25. Matwin 1999 25 Co-learning How to use unlabelled data? Or How to limit the number of examples that need be labelled? Two classifiers and two redundantly sufficient representations Train both, run both on test set, add best predictions to training set

26. Matwin 1999 26 Co-learning Training set grows as… …each learner predicts independently due to redundant sufficiency (different representations) would also work with our learners if we used Bayes? Would work with classifying emails

27. Matwin 1999 27 Co-learning Mitchell experimented with the task of classifying web pages (profs, students, courses, projects) – a supervised learning task Used Anchor text Page contents Error rate halved (from 11% to 5%)

28. Matwin 1999 28 Cog-sci? Co- learning seems to be cognitively justified Model: students learning in groups (pairs) What other social learning mechanisms could provide models for supervised learning?

29. Matwin 1999 29 Conclusion A practical task, needs a solution No satisfactory solution so far Fruitful ground for research