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I256 Applied Natural Language Processing Fall 2009

I256 Applied Natural Language Processing Fall 2009

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I256 Applied Natural Language Processing Fall 2009

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  1. I256 Applied Natural Language ProcessingFall 2009 Lecture 10 Classification Barbara Rosario

  2. Today • Classification tasks • Various issues regarding classification • Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification, variants… • Introduce the steps necessary for a classification task • Define classes (aka labels) • Label text • Define and extract features • Training and evaluation • NLTK example

  3. Classification tasks Assign the correct class label for a given input/object In basic classification tasks, each input is considered in isolation from all other inputs, and the set of labels is defined in advance. Examples: Problem Object Label’s categories Tagging Word POS Sense Disambiguation Word The word’s senses Information retrieval Document Relevant/not relevant Sentiment classification Document Positive/negative Text categorization Document Topics/classes Author identification Document Authors Language identification Document Language Adapted from: Foundations of Statistical NLP (Manning et al)

  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 • Called Stylometry in the humanities • 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 and Wallace 1964. Inference and Disputed Authorship: The Federalist.

  7. Function words for Author Identification

  8. Function words for Author Identification

  9. 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

  10. Language identification • égaux • eguali • iguales • edistämään • Ü • ¿ • How to do determine, for a stretch of text, which language it is from? • Turns out to be really simple • Just a few character bigrams can do it (Sibun & Reynar 96) • Using special character sets helps a bit, but barely

  11. Language Identification (Sibun & Reynar 96)

  12. Confusion Matrix • A table that shows, for each class, which ones your algorithm got right and which wrong Gold standard Algorithm’s guess

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

  14. Text Categorization Applications • Web pages organized into category hierarchies • Journal articles indexed by subject categories (e.g., the Library of Congress, MEDLINE, etc.) • Patents archived using International Patent Classification • Patient records coded using international insurance categories • E-mail message filtering • Spam vs. anti-palm • Customer service message classification • News events tracked and filtered by topics

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

  16. Reuters • Top topics in Reuters

  17. 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>

  18. Outline • Classification tasks • Various issues regarding classification • Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification, variants… • Introduce the steps necessary for a classification task • Define classes (aka labels) • Label text • Define and extract features • Training and evaluation • NLTK example

  19. Classification vs. Clustering • Classification assumes labeled data: we know how many classes there are and we have examples for each class (labeled data). • Classification is supervised • In Clustering we 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

  20. Classification Class1 Class2

  21. Classification Class1 Class2

  22. Classification Class1 Class2

  23. Classification Class1 Class2

  24. Clustering

  25. Clustering

  26. Clustering

  27. Clustering

  28. Clustering

  29. Supervised classification • A classifier is called supervised if it is built based on training corpora containing the correct label for each input.

  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. Variants • 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 • In open-class classification, the set of labels is not defined in advance • In sequence classification, a list of inputs are jointly classified. • E.g. POS tagging

  33. Reuters (multi-category) <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>

  34. Outline • Classification tasks • Various issues regarding classification • Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification, variants… • Introduce the steps necessary for a classification task • Define classes (aka labels) • Label text • Define and extract features • Training and evaluation • NLTK example

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

  36. Categories (Labels, Classes) • Labeling data • 2 problems: • Decide the possible classes (which ones, how many) • Domain and application dependent • Trade-off between accuracy and coverage • Label text • Difficult, time consuming, inconsistency between annotators

  37. Cost of Manual Text Categorization • Time and money! • Yahoo! • 200 (?) people for manual labeling of Web pages • using a hierarchy of 500,000 categories • MEDLINE (National Library of Medicine) • $2 million/year for manual indexing of journal articles • using MEdical Subject Headings (18,000 categories) • Mayo Clinic • $1.4 million annually for coding patient-record events • using the International Classification of Diseases (ICD) for billing insurance companies • US Census Bureau decennial census (1990: 22 million responses) • 232 industry categories and 504 occupation categories • $15 million if fully done by hand

  38. 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?

  39. Feature terminology • Feature: An aspect of the text that is relevant to the task • Feature value: the realization of the feature in the text • Some typical features • Words present in text : Kerry, Schumacher, China… • Frequency of word: Kerry(10), Schumacher(1)… • Are there dates? Yes/no • Capitalization (is word capitalized?) • 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) • Chunks, parse trees, POS

  40. 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

  41. 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

  42. Feature selection • Selecting relevant features and deciding how to encode them for a learning method can have an enormous impact on the learning method's ability to extract a good model • How do we choose the “right” features? • Typically, feature extractors are built through a process of trial-and-error, guided by intuitions about what information is relevant to the problem. • But there are also more “principled” way of features selection

  43. Feature selection • There are usually limits to the number of features that you should use with a given learning algorithm — if you provide too many features, then the algorithm will have a higher chance of relying on idiosyncrasies of your training data that don't generalize well to new examples. • This problem is known as overfitting, and can be especially problematic when working with small training sets.

  44. Feature selection • Once an initial set of features has been chosen, a very productive method for refining the feature set is error analysis. First, we select a development set, containing the corpus data for creating the model. This development set is then subdivided into the training set and the dev-test set. • The training set is used to train the model, and the dev-test set is used to perform error analysis. • Look at errors, change features or model • The test set serves in our final evaluation of the system.

  45. Outline • Classification tasks • Various issues regarding classification • Clustering vs. classification, binary vs. multi-way, flat vs. hierarchical classification, variants… • Introduce the steps necessary for a classification task • Define classes (aka labels) • Label text • Define and extract features • Training and evaluation • NLTK example

  46. Training • 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 • (Next class)

  47. (Linear) Classification Class1 Linear classifier: g(x) = wx + w0 parameters:w, w0 Class2

  48. (Linear) Classification Class1 Linear classifier: g(x) = wx + w0 Changing the parameters:w, w0 Class2

  49. (Linear) Classification Class1 Linear classifier: g(x) = wx + w0 Class2 For each set of parameters:w, w0, calculate error