Introduction to Machine Learning and Text Mining Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute

Naïve Approach: When all you have is a hammer… Data Target Representation

Slightly less naïve approach: Aimless wandering… Data Target Representation

Expert Approach: Hypothesis driven Data Target Representation

Suggested Readings Witten, I. H., Frank, E., Hall, M. (2011). Data Mining: Practical Machine Learning Tools and Techniques, third edition, Elsevier: San Francisco

What is machine learning? Automatically or semi-automatically Inducing concepts (i.e., rules) from data Finding patterns in data Explaining data Making predictions Data Learning Algorithm Model New Data Prediction Classification Engine

Train Test

If Outlook = sunny, no else if Outlook = overcast, yes else if Outlook = rainy and Windy = TRUE, no else yes Perfect on training data

If Outlook = sunny, no else if Outlook = overcast, yes else if Outlook = rainy and Windy = TRUE, no else yes Not perfect on testing data Performance on training data?

If you evaluate the performance of your rule on the same data If Outlook = sunny, no else if Outlook = overcast, yes else if Outlook = rainy and Windy = TRUE, no else yes IMPORTANT! If you evaluate the performance of your rule on the same data you trained on, you won’t get an accurate estimate of how well it will do on new data.

Simple Cross Validation Fold: 1 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 2, 3, 4, 5, 6,7 and apply trained model to 1 The results is Accuracy1 TEST 1 TRAIN 2 TRAIN 3 TRAIN 4 TRAIN 5 TRAIN 6 TRAIN 7

Simple Cross Validation Fold: 2 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1, 3, 4, 5, 6,7 and apply trained model to 2 The results is Accuracy2 TRAIN 1 TEST 2 TRAIN 3 TRAIN 4 TRAIN 5 TRAIN 6 TRAIN 7

Simple Cross Validation Fold: 3 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1, 2, 4, 5, 6,7 and apply trained model to 3 The results is Accuracy3 TRAIN 1 TRAIN 2 TEST 3 TRAIN 4 TRAIN 5 TRAIN 6 TRAIN 7

Simple Cross Validation Fold: 4 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1,2, 3, 5, 6,7 and apply trained model to 4 The results is Accuracy4 TRAIN 1 TRAIN 2 TRAIN 3 TEST 4 TRAIN 5 TRAIN 6 TRAIN 7

Simple Cross Validation Fold: 5 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1, 2, 3, 4, 6,7 and apply trained model to 5 The results is Accuracy5 TRAIN 1 TRAIN 2 TRAIN 3 TRAIN 4 TEST 5 TRAIN 6 TRAIN 7

Simple Cross Validation Fold: 6 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1, 2, 3, 4, 5, 7 and apply trained model to 6 The results is Accuracy6 TRAIN 1 TRAIN 2 TRAIN 3 TRAIN 4 TRAIN 5 TEST 6 TRAIN 7

Simple Cross Validation Fold: 7 Let’s say your data has attributes A, B, and C You want to train a rule to predict D First train on 1, 2, 3, 4, 5, 6 and apply trained model to 7 The results is Accuracy7 Finally: Average Accuracy1 through Accuracy7 TRAIN 1 TRAIN 2 TRAIN 3 TRAIN 4 TRAIN 5 TRAIN 6 TEST 7

Working with Text

Basic Idea Represent text as a vector where each position corresponds to a term This is called the “bag of words” approach Cheese Cows Eat Hamsters Make Seeds Cows make cheese. 110010 Hamsters eat seeds. 001101

Basic Idea Represent text as a vector where each position corresponds to a term This is called the “bag of words” approach But same representation for “Cheese makes cows.”! Cheese Cows Eat Hamsters Make Seeds Cows make cheese. 110010 Hamsters eat seeds. 001101

Part of Speech Tagging 1. CC Coordinating conjunction http://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html 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

Part of Speech Tagging 23.RP Particle 24.SYM Symbol 25.TO to http://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html 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

Basic Types of Features Unigram Single words prefer, sandwhich, take Bigram Pairs of words next to each other Machine_learning, eat_wheat POS-Bigram Pairs of POS tags next to each other DT_NN, NNP_NNP

Keep this picture in mind… Machine learning isn’t magic But it can be useful for identifying meaningful patterns in your data when used properly Proper use requires insight into your data ?