1. Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd Machine Learning for Sequential Data

2. Outline  Sequential Supervised Learning  Research Issues  Methods for Sequential Supervised Learning  Concluding Remarks

3. Some Example Learning Problems  Cellular Telephone Fraud  Part-of-speech Tagging  Information Extraction from the Web  Hyphenation for Word Processing

4. Cellular Telephone Fraud  Given the sequence of recent telephone calls, can we determine which calls (if any) are fraudulent?

5. Part-of-Speech Tagging  Given an English sentence, can we assign a part of speech to each word?  “Do you want fries with that?” 

6. Information Extraction from the Web Srinivasan Seshan (Carnegie Mellon University) Making Virtual Worlds Real Tuesday, June 4, 2002 2:00 PM, 322 Sieg Research Seminar * * * name name * * affiliation affiliation affiliation * * * * title title title title * * * date date date date * time time * location location * event-type event-type

7. Hyphenation  “Porcupine” ! “001010000”

8. Sequential Supervised Learning (SSL)  Given: A set of training examples of the form (X i,Y i ), where X i = hx i,1, …, x i,T i i and Y i = hy i,1, …, y i,T i i are sequences of length T i  Find: A function f for predicting new sequences: Y = f(X).

9. Examples as Sequential Supervised Learning DomainInput X i Output Y i Telephone Fraud sequence of calls sequence of labels {ok, fraud} Part-of-speech Tagging sequence of words sequence of parts of speech Information Extraction sequence of tokens sequence of field labels {name, …} Hyphenationsequence of letters sequence of {0,1} 1 = hyphen ok

10. Two Kinds of Relationships  Relationships between the x t ’s and y t ’s Example: “Friday” is usually a “date”  Relationships among the y t ’s Example: “name” is usually followed by “affiliation”  SSL can (and should) exploit both kinds of information

11. Two Other Tasks that are Not SSL  Sequence Classification  Time-Series Prediction

12. Sequence Classification  Given an input sequence, assign one label to the entire sequence  Example: Recognize a person from their handwriting. Input sequence: Sequence of pen strokes Output label: Name of person

13. Time-Series Prediction  Given: A sequence hy 1, …, y t i predict y t+1.  Example: Predict unemployment rate for next month based on history of unemployment rates.

14. Key Differences  In SSL, there is one label y i,t for each input x i,t  In SSL, we are given the entire X sequence before we need to predict any of the y t values  In SSL, we do not have any of the true y values when we predict y t+1

15. Outline  Sequential Supervised Learning  Research Issues  Methods for Sequential Supervised Learning  Concluding Remarks

16. Research Issues for SSL  Loss Functions How do we measure performance?  Feature Selection and Long-distance Interactions How do we model relationships among the y t ’s, especially long-distance effects?  Computational Cost How do we make it efficient?

17. Basic Loss Functions  Count the number of entire sequences Y i correctly predicted (i.e., every y i,t must be right)  Count the number of individual labels y i,t correctly predicted

18. More Complex Loss Functions x x x x x x x x x x x x x x x x x x x x x x x x x True labels: Phone calls: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Loss: Loss is computed for first “fraudulent” prediction

19. More Complex Loss Functions (2)  Hyphenation False positives are very bad Need at least one correct hyphen near middle of word

20. Hyphenation Loss  Perfect: “qual-i-fi-ca-tion”  Very good: “quali-fi-cation”  OK: “quali-fication”, “qualifi-cation”  Worse: “qual-ification”, “qualifica-tion”  Very bad: “qua-lification”, “qualificatio-n”

21. Feature Selection and Long Distance Effects  Any solution to SSL must employ some form of divide-and-conquer  How do we determine the information relevant for predicting y t ?

22. Long Distance Effects  Consider the text-to-speech problem: “photograph” => / f-Ot@graf- / “photography” =>/ f-@tAgr@f-i /  The letter “y” changes the pronunciation of all vowels in the word!

23. Standard Feature Selection Methods  Wrapper method with forward selection or backwards elimination  Optimize feature weights  Measures of feature influence  Fit simple models to test for relevance

24. Wrapper Methods  Stepwise Regression  Wrapper Methods (Kohavi, et al.)  Problem: Very inefficient with large numbers of possible features

25. Optimizing the Feature Weights  Start with all features in the model  Encourage the learning algorithm to remove irrelevant features  Problem: There are too many possible features. We can’t include them all in the model.

26. Measures of Feature Influence  Importance of single features Mutual information, Correlation  Importance of feature subsets Schema racing (Moore, et al.) RELIEFF (Kononenko, et al.)  Question: Will subset methods scale to thousands of features?

27. Fitting Simple Models  Fit simple models using all of the features. Analyze the resulting model to determine feature importance Belief networks and Markov blanket analysis L 1 Support Vector Machines  Prediction: These will be the most practical methods

28. Outline  Sequential Supervised Learning  Research Issues  Methods for Sequential Supervised Learning  Concluding Remarks

29. Methods for Sequential Supervised Learning  Sliding Windows  Recurrent Sliding Windows  Hidden Markov Models and company Maximum Entropy Markov Models Input-Output HMMs Conditional Random Fields

30. Sliding Windows ___Doyouwantfrieswiththat___ Doyou!verb youwantfries!verb wantfrieswith!noun frieswiththat!prep withthat___!pron Doyouwant!pron

31. Properties of Sliding Windows  Converts SSL to ordinary supervised learning  Only captures the relationship between (part of) X and y t. Does not explicitly model relations among the y t ’s  Does not capture long-distance interactions  Assumes each window is independent

32. Recurrent Sliding Windows ___Doyouwantfrieswiththat___ Doyou___!verb youwantfriespron!verb wantfrieswithverb!noun frieswiththatnoun!prep withthat___prep!pron Doyouwantverb!pron

33. Recurrent Sliding Windows  Key Idea: Include y t as input feature when computing y t+1.  During training: Use the correct value of y t Or train iteratively (especially recurrent neural networks)  During evaluation: Use the predicted value of y t

34. Properties of Recurrent Sliding Windows  Captures relationship among the y’s, but only in one direction!  Results on text-to-speech: MethodDirectionWordsLetters sliding windownone12.5%69.6% recurrent s. w.left-right17.0%67.9% recurrent s. w.right-left24.4%74.2%

35. Hidden Markov Models y2y2 y1y1 y3y3 x1x1 x2x2 x3x3  y t ’s are generated as a Markov chain  x t ’s are generated independently (as in naïve Bayes or Gaussian classifiers).

36. Hidden Markov Models (2)  Models both the x t $ y t relationships and the y t $ y t+1 relationships.  Does not handle long-distance effects Everything must be captured by the current label y t.  Does not permit rich X $ y t relationships Unlike the sliding window, we can’t use several x t ’s to predict y t.

37. Using HMMs  Training Extremely simple, because the y t ’s are known on the training set.  Execution: Dynamic Programming methods If the loss function depends on the whole sequence, use the Viterbi algorithm: argmax Y P(Y | X) If the loss function depends on individual y t predictions, use the forward-backward algorithm: argmax y t P(y t | X)

38. HMM Alternatives: Maximum Entropy Markov Models y2y2 y1y1 y3y3 x1x1 x2x2 x3x3

39. MEMM Properties  Permits complex X $ y t relationships by employing a sparse maximum entropy model of P(y t+1 |X, y t ): P(y t+1 |X,y t ) / exp(  b  b f b (X,y t,y t+1 )) where f b is a boolean feature.  Training can be expensive (gradient descent or iterative scaling)

40. HMM Alternatives (2): Input/Output HMM h2h2 h1h1 h3h3 x1x1 x2x2 x3x3 y1y1 y2y2 y3y3

41. IOHMM Properties  Hidden states permit “memory” of long distance effects (beyond what is captured by the class labels)  As with MEMM, arbitrary features of the input X can be used to predict y t.

42.  Forward models that are normalized at each step exhibit a problem.  Consider a domain with only two sequences: “rib” ! “111” and “rob” ! “222”.  Consider what happens when an MEMM sees the sequence “rib”. Label Bias Problem

43. Label Bias Problem (2)  After “r”, both labels 1 and 2 have same probability. After “i”, label 2 must still send all of its probability forward, even though it was expecting “o”. Result: both output strings “111” and “222” have same probability. ib 111 222 r r ob

44. Conditional Random Fields y2y2 y1y1 y3y3 x1x1 x2x2 x3x3  The y t ’s form a Markov Random Field conditioned on X.

45. Representing the CRF parameters  Each undirected arc y t $ y t+1 represents a potential function: M(y t,y t+1 |X) = exp[  a a f a (y t,y t+1,X) +  b  b g b (y t,X)] where f a and g b are arbitrary boolean features.

46. Using CRFs P(Y|X) / M(y 1,y 2 |X) ¢ M(y 2,y 3 |X) ¢ … ¢ M(y T-1,y T |X)  Training: Gradient descent or iterative scaling

47. CRFs on Part-of-speech tagging HMMMEMMCRF baseline 5.696.375.55 spelling features 5.694.874.27 spelling features (OOV) 45.9926.9923.76 Lafferty, McCallum & Pereira (2001) (error rates in percent)

48. Summary of Methods IssueSWRSWHMMMEMMIOHMMCRF x t $ y t y t $ y t+1 NOPartlyYES long dist?NOPartlyNO YES?NO X $ y t rich?YES NOYES efficient?YES YES?NO label bias ok?YES NO YES

49. Loss Functions and Training  Kakade, Teh & Roweis (2002) show that if the loss function depends only on errors of y t, then MEMM’s, IOHMM’s, and CRF’s should be trained to maximize the likelihood P(y t | X) instead of P(Y|X) or P(X,Y).

50. Concluding Remarks  Many applications of pattern recognition can be formalized as Sequential Supervised Learning  Many methods have been developed specifically for SSL, but none is perfect  Similar issues arise in other complex learning problems (e.g., spatial and relational data)