CPE 641 Natural Language Processing

1. CPE 641 Natural Language Processing Asst. Prof. Nuttanart Facundes Text Classification Adapted from Barbara Rosario’s slides – Sept. 27,2004

2. Classification • Classification • Text categorization (and other applications) • Various issues regarding classification • Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification… • Introduce the steps necessary for a classification task • Define classes • Label text • Features • Training and evaluation of a classifier

3. Classification Goal: Assign ‘objects’ from a universe to two or more classes or categories Examples: Problem Object Categories Tagging Word POS Sense Disambiguation Word The word’s senses Information retrieval Document Relevant/not relevant Sentiment classification Document Positive/negative Author identification Document Authors From: Foundations of Statistical Natural Language Processing. Manning and Schutze

4. Author identification • They agreed that Mrs. X should only hear of the departure of the family, without being alarmed on the score of the gentleman's conduct; but even this partial communication gave her a great deal of concern, and she bewailed it as exceedingly unlucky that the ladies should happen to go away, just as they were all getting so intimate together. • Gas looming through the fog in divers places in the streets, much as the sun may, from the spongey fields, be seen to loom by husbandman and ploughboy. Most of the shops lighted two hours before their time--as the gas seems to know, for it has a haggard and unwilling look. The raw afternoon is rawest, and the dense fog is densest, and the muddy streets are muddiest near that leaden-headed old obstruction, appropriate ornament for the threshold of a leaden-headed old corporation, Temple Bar.

5. Author identification • Jane Austen (1775-1817), Pride and Prejudice • Charles Dickens (1812-70), Bleak House

6. Author identification • Federalist papers • 77 short essays written in 1787-1788 by Hamilton, Jay and Madison to persuade NY to ratify the US Constitution; published under a pseudonym • The authorships of 12 papers was in dispute (disputed papers) • In 1964 Mosteller and Wallace* solved the problem • They identified 70 function words as good candidates for authorships analysis • Using statistical inference they concluded the author was Madison Mosteller, Frederick and Wallace, David L. 1964. Inference and Disputed Authorship: The Federalist.

7. Function words for Author Identification

8. Function words for Author Identification

9. Classification Goal: Assign ‘objects’ from a universe to two or more classes or categories Examples: Problem Object Categories Author identification Document Authors Language identification Document Language From: Foundations os Statistical Natural Language Processing. Manning and Schutze

10. Language identification • Tutti gli esseri umani nascono liberi ed eguali in dignità e diritti. Essi sono dotati di ragione e di coscienza e devono agire gli uni verso gli altri in spirito di fratellanza. • Alle Menschen sind frei und gleich an Würde und Rechten geboren. Sie sind mit Vernunft und Gewissen begabt und sollen einander im Geist der Brüderlichkeit begegnen. Universal Declaration of Human Rights, UN, in 363 languages

11. Language identification • égaux • eguali • iguales • edistämään • Ü • ¿

12. Classification Goal: Assign ‘objects’ from a universe to two or more classes or categories Examples: Problem Object Categories Author identification Document Authors Language identification Document Language Text categorization Document Topics From: Foundations of Statistical Natural Language Processing. Manning and Schutze

13. Text categorization • Topic categorization: classify the document into semantics topics

14. Text categorization • http://news.google.com/ • Reuters • Collection of (21,578) newswire documents. • For research purposes: a standard text collection to compare systems and algorithms • 135 valid topics categories

15. Reuters • Top topics in Reuters

16. Reuters <REUTERS TOPICS="YES" LEWISSPLIT="TRAIN" CGISPLIT="TRAINING-SET" OLDID="12981" NEWID="798"> <DATE> 2-MAR-1987 16:51:43.42</DATE> <TOPICS><D>livestock</D><D>hog</D></TOPICS> <TITLE>AMERICAN PORK CONGRESS KICKS OFF TOMORROW</TITLE> <DATELINE> CHICAGO, March 2 - </DATELINE><BODY>The American Pork Congress kicks off tomorrow, March 3, in Indianapolis with 160 of the nations pork producers from 44 member states determining industry positions on a number of issues, according to the National Pork Producers Council, NPPC. Delegates to the three day Congress will be considering 26 resolutions concerning various issues, including the future direction of farm policy and the tax law as it applies to the agriculture sector. The delegates will also debate whether to endorse concepts of a national PRV (pseudorabies virus) control and eradication program, the NPPC said. A large trade show, in conjunction with the congress, will feature the latest in technology in all areas of the industry, the NPPC added. Reuter &#3;</BODY></TEXT></REUTERS>

17. Text categorization: examples • Topic categorization • http://news.google.com/ • Reuters. • Spam filtering • Determine if a mail message is spam (or not) • Customer service message classification

18. Classification vs. Clustering • Classificationassumes labeled data: we know how many classes there are and we have examples for each class (labeled data). • Classification is supervised • In Clusteringwe don’t have labeled data; we just assume that there is a natural division in the data and we may not know how many divisions (clusters) there are • Clustering is unsupervised

19. Classification Class1 Class2

20. Classification Class1 Class2

21. Classification Class1 Class2

22. Classification Class1 Class2

23. Clustering

24. Clustering

25. Clustering

26. Clustering

27. Clustering

28. Categories (Labels, Classes) • Labeling data • 2 problems: • Decide the possible classes (which ones, how many) • Domain and application dependent • http://news.google.com • Label text • Difficult, time consuming, inconsistency between annotators

29. Reuters <REUTERS TOPICS="YES" LEWISSPLIT="TRAIN" CGISPLIT="TRAINING-SET" OLDID="12981" NEWID="798"> <DATE> 2-MAR-1987 16:51:43.42</DATE> <TOPICS><D>livestock</D><D>hog</D></TOPICS> <TITLE>AMERICAN PORK CONGRESS KICKS OFF TOMORROW</TITLE> <DATELINE> CHICAGO, March 2 - </DATELINE><BODY>The American Pork Congress kicks off tomorrow, March 3, in Indianapolis with 160 of the nations pork producers from 44 member states determining industry positions on a number of issues, according to the National Pork Producers Council, NPPC. Delegates to the three day Congress will be considering 26 resolutions concerning various issues, including the future direction of farm policy and the tax law as it applies to the agriculture sector. The delegates will also debate whether to endorse concepts of a national PRV (pseudorabies virus) control and eradication program, the NPPC said. A large trade show, in conjunction with the congress, will feature the latest in technology in all areas of the industry, the NPPC added. Reuter &#3;</BODY></TEXT></REUTERS> Why not topic = policy ?

30. Binary vs. multi-way classification • Binary classification: two classes • Multi-way classification: more than two classes • Sometime it can be convenient to treat a multi-way problem like a binary one: one class versus all the others, for all classes

31. Flat vs. Hierarchical classification • Flat classification: relations between the classes undetermined • Hierarchical classification: hierarchy where each node is the sub-class of its parent’s node

32. Single- vs. multi-category classification • In single-category text classification each text belongs to exactly one category • In multi-category text classification, each text can have zero or more categories

33. LabeledText class in NLTK • LabeledTextclass • >>> text = "Seven-time Formula One champion Michael Schumacher took on the Shanghai circuit Saturday in qualifying for the first Chinese Grand Prix." • >>> label = “sport” • >>> labeled_text = LabeledText(text, label) • >>> labeled_text.text() • “Seven-time Formula One champion Michael Schumacher took on the Shanghai circuit Saturday in qualifying for the first Chinese Grand Prix.” • >>> labeled_text.label() • “sport”

34. NLTK: The Classifier Interface • classifydetermines which label is most appropriate for a given text token, and returns a labeled text token with that label. • labelsreturns the list of category labels that are used by the classifier. • >>> token = Token(“The World Health Organization is recommending more importance be attached to the prevention of heart disease and other cardiovascular ailments rather than focusing on treatment.”) • >>> my_classifier.classify(token) “The World Health Organization is recommending more importance be attached to the prevention of heart disease and other cardiovascular ailments rather than focusing on treatment.”/ health >>> my_classifier.labels() • ("sport", "health", "world",…)

35. Features • >>> text = "Seven-time Formula One champion Michael Schumacher took on the Shanghai circuit Saturday in qualifying for the first Chinese Grand Prix." • >>> label = “sport” • >>> labeled_text = LabeledText(text, label) • Here the classification takes as input the whole string • What’s the problem with that? • What are the features that could be useful for this example?

36. Feature terminology • Feature: An aspect of the text that is relevant to the task • Some typical features • Words present in text • Frequency of words • Capitalization • Are there NE? • WordNet • Others?

37. Feature terminology • Feature: An aspect of the text that is relevant to the task • Feature value: the realization of the feature in the text • Words present in text : Kerry, Schumacher, China… • Frequency of word: Kerry(10), Schumacher(1)… • Are there dates? Yes/no • Are there PERSONS? Yes/no • Are there ORGANIZATIONS? Yes/no • WordNet: Holonyms (China is part of Asia), Synonyms(China, People's Republic of China, mainland China)

38. Feature Types • Boolean (or Binary) Features • Features that generate boolean (binary) values. • Boolean features are the simplest and the most common type of feature. • f1(text) = 1 if text contain “Kerry” 0 otherwise • f2(text) = 1 if text contain PERSON 0 otherwise

39. Feature Types • Integer Features • Features that generate integer values. • Integer features can be used to give classifiers access to more precise information about the text. • f1(text) = Number of times text contains “Kerry” • f2(text) = Number of times text contains PERSON

40. Features in NLTK • Feature Detectors • Features can be defined using feature detector functions, which map LabeledTexts to values • Method: detect, which takes a labeled text, and returns a feature value. • >>> def ball(ltext): return (“ball” in ltext.text()) • >>> fdetector = FunctionFeatureDetector(ball) • >>> document1 = "John threw the ball over the fence".split() • >>> fdetector.detect(LabeledText(document1) 1 • >>> document2 = "Mary solved the equation".split() • >>> fdetector.detect(LabeledText(document2) 0

41. Feature selection • How do we choose the “right” features?

42. Classification • Define classes • Label text • Extract Features • Choose a classifier • >>> my_classifier.classify(token) • The Naive Bayes Classifier • NN (perceptron) • SVM • …. • Train it (and test it) • Use it to classify new examples

43. Training • (We’ll see what we mean exactly with training when we’ll talk about the algorithms) • Adaptation of the classifier to the data • Usually the classifier is defined by a set of parameters • Training is the procedure for finding a “good” set of parameters • Goodness is determined by an optimization criterion such as misclassification rate • Some classifiers are guaranteed to find the optimal set of parameters

44. Testing, evaluation of the classifier • After choosing the parameters of the classifiers (i.e. after training it) we need to test how well it’s doing on a test set (not included in the training set) • Calculate misclassification on the test set

45. Evaluating classifiers • Contingency table for the evaluation of a binary classifier • Accuracy = (a+d)/(a+b+c+d) • Precision: P_GREEN = a/(a+b), P_ RED= d/(c+d) • Recall: R_GREEN = a/(a+c), R_ RED= d/(b+d)

46. Training size • The more the better! (usually) • Results for text classification* *From: Improving the Performance of Naive Bayes for Text Classification, Shen and Yang

47. Training size *From: Improving the Performance of Naive Bayes for Text Classification, Shen and Yang

48. Training size *From: Improving the Performance of Naive Bayes for Text Classification, Shen and Yang

49. Training Size • Author identification Authorship Attribution a Comparison Of Three Methods, Matthew Care