1. TagHelper & SIDE Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute

2. TagHelper Tools and SIDE TagHelper Tools uses text mining technology to automate annotation of conversational data SIDE facilitates rapid prototyping of reporting interfaces for group learning facilitators Define Summaries Annotate Data Visualize Annotated Data

22. Setting Up Your Data For TagHelper

23. Setting Up Your Data

24. How do you know when you have coded enough data? What distinguishes Questions and Statements? Not all questions end in a question mark. Not all WH words occur in questions I versus you is not a reliable predictor You need to code enough to avoid learning rules that won’t work

25. Creating a Trained Model

26. Training and Testing Start TagHelper tools by double clicking on the portal.bat icon in your TagHelperTools2 folder You will then see the following tool pallet The idea is that you will train a prediction model on your coded data and then apply that model to uncoded data Click on Train New Models

27. Loading a File First click on Add a File Then select a file

28. Simplest Usage Click “GO!” TagHelper will use its default setting to train a model on your coded examples It will use that model to assign codes to the uncoded examples

29. More Advanced Usage The second option is to modify the default settings You get to the options you can set by clicking on >> Options After you finish that, click “GO!”

30. Evaluating Performance

31. Performance report The performance report tells you:  What dataset was used  What the customization settings were  At the bottom of the file are reliability statistics and a confusion matrix that tells you which types of errors are being made

32. Output File The output file contains  The codes for each segment  Note that the segments that were already coded will retain their original code  The other segments will have their automatic predictions  The prediction column indicates the confidence of the prediction

33. Overview of Basic Feature Extraction from Text

34. Customizations To customize the settings:  Select the file  Click on Options

35. Classifier Options * The three main types of Classifiers are Bayesian models (Naïve Bayes), functions (SMO), and trees (J48) Rules of thumb:  SMO is state-of-the-art for text classification  J48 is best with small feature sets – also handles contingencies between features well  Naïve Bayes works well for models where decisions are made based on accumulating evidence rather than hard and fast rules

36. Basic Idea Represent text as a vector where each position corresponds to a term This is called the “bag of words” approach Cows make cheese 110001 Hens lay eggs 001110 Cheese Cows Eggs Hens Lay Make But same representation for “Cheese makes cows.”!

37. What can’t you conclude from “bag of words” representations? Causality: “X caused Y” versus “Y caused X” Roles and Mood: “Which person ate the food that I prepared this morning and drives the big car in front of my cat” versus “The person, which prepared food that my cat and I ate this morning, drives in front of the big car.”  Who’s driving, who’s eating, and who’s preparing food?

38. Basic Anatomy: Layers of Linguistic Analysis Phonology: The sound structure of language  Basic sounds, syllables, rhythm, intonation Morphology: The building blocks of words  Inflection: tense, number, gender  Derivation: building words from other words, transforming part of speech Syntax: Structural and functional relationships between spans of text within a sentence  Phrase and clause structure Semantics: Literal meaning, propositional content Pragmatics: Non-literal meaning, language use, language as action, social aspects of language (tone, politeness) Discourse Analysis: Language in practice, relationships between sentences, interaction structures, discourse markers, anaphora and ellipsis

39. Part of Speech Tagging 1. CC Coordinating conjunction 2. CD Cardinal number 3. DT Determiner 4. EX Existential there 5. FW Foreign word 6. IN Preposition/subord 7. JJ Adjective 8. JJR Adjective, comparative 9. JJS Adjective, superlative 10.LS List item marker 11.MD Modal 12.NN Noun, singular or mass 13.NNS Noun, plural 14.NNP Proper noun, singular 15.NNPS Proper noun, plural 16.PDT Predeterminer 17.POS Possessive ending 18.PRP Personal pronoun 19.PP Possessive pronoun 20.RB Adverb 21.RBR Adverb, comparative 22.RBS Adverb, superlative http://www.ldc.upenn.edu/Catalog/docs/treebank2/cl93.html

40. Part of Speech Tagging 23.RP Particle 24.SYM Symbol 25.TO to 26.UH Interjection 27.VB Verb, base form 28.VBD Verb, past tense 29.VBG Verb, gerund/present participle 30.VBN Verb, past participle 31.VBP Verb, non-3rd ps. sing. present 32.VBZ Verb, 3rd ps. sing. present 33.WDT wh-determiner 34.WP wh-pronoun 35.WP Possessive wh- pronoun 36.WRB wh-adverb http://www.ldc.upenn.edu/Catalog/docs/treebank2/cl93.html

41. TagHelper Customizations Feature Space Design  Think like a computer!  Machine learning algorithms look for features that are good predictors, not features that are necessarily meaningful  Look for approximations If you want to find questions, you don’t need to do a complete syntactic analysis Look for question marks Look for wh-terms that occur immediately before an auxilliary verb

42. TagHelper Customizations Feature Space Design  Punctuation can be a “stand in” for mood “you think the answer is 9?” “you think the answer is 9.”  Bigrams capture simple lexical patterns “common denominator” versus “common multiple”  POS bigrams capture syntactic or stylistic information “the answer which is …” vs “which is the answer”  Line length can be a proxy for explanation depth

43. TagHelper Customizations Feature Space Design  Contains non-stop word can be a predictor of whether a conversational contribution is contentful “ok sure” versus “the common denominator”  Remove stop words removes some distracting features  Stemming allows some generalization Multiple, multiply, multiplication  Removing rare features is a cheap form of feature selection Features that only occur once or twice in the corpus won’t generalize, so they are a waste of time to include in the vector space

44. Created Features

45. Why create new features by hand? Rules  For simple rules, it might be easier and faster to write the rules by hand instead of learning them from examples Features  More likely to capture meaningful generalizations  Build in knowledge so you can get by with less training data

46. Rule Language ANY() is used to create lists  COLOR = ANY(red,yellow,green,blue,purple)  FOOD = ANY(cake,pizza,hamburger,steak,bread) ALL() is used to capture contingencies  ALL(cake,presents) More complex rules  ALL(COLOR,FOOD) * Note that you may wish to use part-of-speech tags in your rules!

47. What can you do with this rule language? You may want to generalize across sets of related words  Color = {red,yellow,orange,green,blue}  Food = {cake,pizza,hamburger,steak,bread} You may want to detect contingencies  The text must mention both cake and presents in order to count as a birthday party You may want to combine these  The text must include a Color and a Food

48. Advanced Feature Editing

49. * For small datasets, first deselect Remove rare features.

50. Advanced Feature Editing * Next, Click on Adv Feature Editing

51. Advanced Feature Editing * Now you may begin creating your own features.

52. Types of Basic Features Primitive features inclulde unigrams, bigrams, and POS bigrams

53. Types of Basic Features The Options change which primitive features show up in the Unigram, Bigram, and POS bigram lists  You can choose to remove stopwords or not  You can choose whether or not to strip endings off words with stemming  You can choose how frequently a feature must appear in your data in order for it to show up in your lists

54. Types of Basic Features * Now let’s look at how to create new features.

55. Creating New Features * You can use the feature editor to create new features.

56. Creating New Features * First click on ANY

57. Creating New Features * Then click ALL

58. Creating New Features * Now fill in ‘tell’ and ‘me’

59. Creating New Features * Now fill in the rest of the pattern from the POS Bigram list

60. Creating New Features * Now change the name

61. Creating New Features * Click to add to feature list

62. Using the Display Option

66. Viewing Created Features

69. Any Questions?