Feature Selection & Maximum Entropy Advanced Statistical Methods in NLP Ling 572 January 26, 2012 1.

Feature Selection & Maximum Entropy Advanced Statistical Methods in NLP Ling 572 January 26, 2012 1

Roadmap Feature selection and weighting Feature weighting Chi-square feature selection Chi-square feature selection example HW #4 Maximum Entropy Introduction: Maximum Entropy Principle Maximum Entropy NLP examples 2

Feature Selection Recap Problem: Curse of dimensionality Data sparseness, computational cost, overfitting 3

Feature Selection Recap Problem: Curse of dimensionality Data sparseness, computational cost, overfitting Solution: Dimensionality reduction New feature set r’ s.t. |r’| < |r| 4

Feature Selection Recap Problem: Curse of dimensionality Data sparseness, computational cost, overfitting Solution: Dimensionality reduction New feature set r’ s.t. |r’| < |r| Approaches: Global & local approaches Feature extraction: New features in r’ transformations of features in r 5

Feature Selection Recap Problem: Curse of dimensionality Data sparseness, computational cost, overfitting Solution: Dimensionality reduction New feature set r’ s.t. |r’| < |r| Approaches: Global & local approaches Feature extraction: New features in r’ transformations of features in r Feature selection: Wrapper techniques 6

Feature Selection Recap Problem: Curse of dimensionality Data sparseness, computational cost, overfitting Solution: Dimensionality reduction New feature set r’ s.t. |r’| < |r| Approaches: Global & local approaches Feature extraction: New features in r’ transformations of features in r Feature selection: Wrapper techniques Feature scoring 7

Feature Weighting For text classification, typical weights include: 8

Feature Weighting For text classification, typical weights include: Binary: weights in {0,1} 9

Feature Weighting For text classification, typical weights include: Binary: weights in {0,1} Term frequency (tf): # occurrences of t k in document d i 10

Feature Weighting For text classification, typical weights include: Binary: weights in {0,1} Term frequency (tf): # occurrences of t k in document d i Inverse document frequency (idf): df k : # of docs in which t k appears; N: # docs idf = log (N/(1+df k )) 11

Feature Weighting For text classification, typical weights include: Binary: weights in {0,1} Term frequency (tf): # occurrences of t k in document d i Inverse document frequency (idf): df k : # of docs in which t k appears; N: # docs idf = log (N/(1+df k )) tfidf = tf*idf 12

Chi Square Tests for presence/absence of relation between random variables 13

Chi Square Tests for presence/absence of relation between random variables Bivariate analysis tests 2 random variables Can test strength of relationship (Strictly speaking) doesn’t test direction 14

Chi Square Tests for presence/absence of relation between random variables Bivariate analysis tests 2 random variables Can test strength of relationship 15

Chi Square Tests for presence/absence of relation between random variables Bivariate analysis tests 2 random variables Can test strength of relationship (Strictly speaking) doesn’t test direction 16

Chi Square Example Can gender predict shoe choice? Due to F. Xia 17

Chi Square Example Can gender predict shoe choice? A: male/female  Features Due to F. Xia 18

Chi Square Example Can gender predict shoe choice? A: male/female  Features B: shoe choice  Classes: {sandal, sneaker,…} Due to F. Xia 19

Chi Square Example Can gender predict shoe choice? A: male/female  Features B: shoe choice  Classes: {sandal, sneaker,…} Due to F. Xia sandalsneakerleather shoe bootother Male6171395 Female1357169 20

Comparing Distributions Observed distribution (O): sandalsneakerleather shoe bootother Male6171395 Female1357169 Due to F. Xia 21

Comparing Distributions Observed distribution (O): Expected distribution (E): sandalsneakerleather shoe bootother Male6171395 Female1357169 Due to F. Xia 22

Comparing Distributions Observed distribution (O): Expected distribution (E): sandalsneakerleather shoe bootother Male6171395 Female1357169 sandalsneakerleather shoe boototherTotal Male50 Female50 Total1922202514100 Due to F. Xia 23

Comparing Distributions Observed distribution (O): Expected distribution (E): sandalsneakerleather shoe bootother Male6171395 Female1357169 sandalsneakerleather shoe boototherTotal Male9.550 Female9.550 Total1922202514100 Due to F. Xia 24

Comparing Distributions Observed distribution (O): Expected distribution (E): sandalsneakerleather shoe bootother Male6171395 Female1357169 sandalsneakerleather shoe boototherTotal Male9.5111012.550 Female9.5111012.550 Total1922202514100 Due to F. Xia 27

Comparing Distributions Observed distribution (O): Expected distribution (E): sandalsneakerleather shoe bootother Male6171395 Female1357169 sandalsneakerleather shoe boototherTotal Male9.5111012.5750 Female9.5111012.5750 Total1922202514100 Due to F. Xia 28

Computing Chi Square Expected value for cell= row_total*column_total/table_total 29

Computing Chi Square Expected value for cell= row_total*column_total/table_total 30

Computing Chi Square Expected value for cell= row_total*column_total/table_total X 2 =(6-9.5) 2 /9.5+ 31

Computing Chi Square Expected value for cell= row_total*column_total/table_total X 2 =(6-9.5) 2 /9.5+(17-11) 2 /11 32

Computing Chi Square Expected value for cell= row_total*column_total/table_total X 2 =(6-9.5) 2 /9.5+(17-11) 2 /11+.. = 14.026 33

Calculating X 2 Tabulate contigency table of observed values: O 34

Calculating X 2 Tabulate contigency table of observed values: O Compute row, column totals 35

Calculating X 2 Tabulate contigency table of observed values: O Compute row, column totals Compute table of expected values, given row/col Assuming no association 36

Calculating X 2 Tabulate contigency table of observed values: O Compute row, column totals Compute table of expected values, given row/col Assuming no association Compute X 2 37

For 2x2 Table O: E: !c i cici !t k ab tktk cd 38

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k tktk total 39

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k a+b tktk c+d totala+cb+dN 40

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k (a+b)(a+c)/Na+b tktk c+d totala+cb+dN 41

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k (a+b)(a+c)/N(a+b)(b+d)/Na+b tktk c+d totala+cb+dN 42

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k (a+b)(a+c)/N(a+b)(b+d)/Na+b tktk (c+d)(a+c)/Nc+d totala+cb+dN 43

For 2x2 Table O: E: !c i cici !t k ab tktk cd !c i cici Total !t k (a+b)(a+c)/N(a+b)(b+d)/Na+b tktk (c+d)(a+c)/N(c+d)(b+d)/Nc+d totala+cb+dN 44

X 2 Test Test whether random variables are independent 45

X 2 Test Test whether random variables are independent Null hypothesis: R.V.s are independent 46

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: 47

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: Compute degrees of freedom 48

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: Compute degrees of freedom df = (# rows -1)(# cols -1) 49

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: Compute degrees of freedom df = (# rows -1)(# cols -1) Shoe example, df = (2-1)(5-1)=4 50

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: Compute degrees of freedom df = (# rows -1)(# cols -1) Shoe example, df = (2-1)(5-1)=4 Test probability of X 2 statistic value X 2 table 51

X 2 Test Test whether random variables are independent Null hypothesis: 2 R.V.s are independent Compute X 2 statistic: Compute degrees of freedom df = (# rows -1)(# cols -1) Shoe example, df = (2-1)(5-1)=4 Test probability of X 2 statistic value X 2 table If probability is low – below some significance level Can reject null hypothesis 52

Requirements for X 2 Test Events assumed independent, same distribution 53

Requirements for X 2 Test Events assumed independent, same distribution Outcomes must be mutually exclusive 54

Requirements for X 2 Test Events assumed independent, same distribution Outcomes must be mutually exclusive Raw frequencies, not percentages 55

Requirements for X 2 Test Events assumed independent, same distribution Outcomes must be mutually exclusive Raw frequencies, not percentages Sufficient values per cell: > 5 56

X 2 Example 57

X 2 Example Shared Task Evaluation: Topic Detection and Tracking (aka TDT) 58

X 2 Example Shared Task Evaluation: Topic Detection and Tracking (aka TDT) Sub-task: Topic Tracking Task Given a small number of exemplar documents (1-4) Define a topic Create a model that allows tracking of the topic I.e. find all subsequent documents on this topic 59

X 2 Example Shared Task Evaluation: Topic Detection and Tracking (aka TDT) Sub-task: Topic Tracking Task Given a small number of exemplar documents (1-4) Define a topic Create a model that allows tracking of the topic I.e. find all subsequent documents on this topic Exemplars: 1-4 newswire articles 300-600 words each 60

Challenges Many news articles look alike Create a profile (feature representation) Highlights terms strongly associated with current topic Differentiate from all other topics 61

Challenges Many news articles look alike Create a profile (feature representation) Highlights terms strongly associated with current topic Differentiate from all other topics Not all documents labeled Only a small subset belong to topics of interest Differentiate from other topics AND ‘background’ 62

Approach X 2 feature selection: 63

Approach X 2 feature selection: Assume terms have binary representation 64

Approach X 2 feature selection: Assume terms have binary representation Positive class term occurrences from exemplar docs 65

Approach X 2 feature selection: Assume terms have binary representation Positive class term occurrences from exemplar docs Negative class term occurrences from other class exemplars, ‘earlier’ uncategorized docs 66

Approach X 2 feature selection: Assume terms have binary representation Positive class term occurrences from exemplar docs Negative class term occurrences from other class exemplars, ‘earlier’ uncategorized docs Compute X 2 for terms Retain terms with highest X 2 scores Keep top N terms 67

Approach X 2 feature selection: Assume terms have binary representation Positive class term occurrences from exemplar docs Negative class term occurrences from other class exemplars, ‘earlier’ uncategorized docs Compute X 2 for terms Retain terms with highest X 2 scores Keep top N terms Create one feature set per topic to be tracked 68

Tracking Approach Build vector space model 69

Tracking Approach Build vector space model Feature weighting: tf*idf with some modifications 70

Tracking Approach Build vector space model Feature weighting: tf*idf with some modifications Distance measure: Cosine similarity 71

Tracking Approach Build vector space model Feature weighting: tf*idf with some modifications Distance measure: Cosine similarity Select documents scoring above threshold For each topic 72

Tracking Approach Build vector space model Feature weighting: tf*idf with some modifications Distance measure: Cosine similarity Select documents scoring above threshold For each topic Result: Improved retrieval 73

HW #4 Topic: Feature Selection for kNN Build a kNN classifier using: Euclidean distance, Cosine Similarity Write a program to compute X 2 on a data set Use X 2 at different significance levels to filter Compare the effects of different feature filtering on kNN classification 74

Maximum Entropy 75

Maximum Entropy “MaxEnt”: Popular machine learning technique for NLP First uses in NLP circa 1996 – Rosenfeld, Berger Applied to a wide range of tasks Sentence boundary detection (MxTerminator, Ratnaparkhi), POS tagging (Ratnaparkhi, Berger), topic segmentation (Berger), Language modeling (Rosenfeld), prosody labeling, etc…. 76

Readings & Comments Several readings: (Berger, 1996), (Ratnaparkhi, 1997) (Klein & Manning, 2003): Tutorial 77

Readings & Comments Several readings: (Berger, 1996), (Ratnaparkhi, 1997) (Klein & Manning, 2003): Tutorial Note: Some of these are very ‘dense’ Don’t spend huge amounts of time on every detail Take a first pass before class, review after lecture 78

Readings & Comments Several readings: (Berger, 1996), (Ratnaparkhi, 1997) (Klein & Manning, 2003): Tutorial Note: Some of these are very ‘dense’ Don’t spend huge amounts of time on every detail Take a first pass before class, review after lecture Going forward: Techniques more complex Goal: Understand basic model, concepts Training esp. complex – we’ll discuss, but not implement 79

Notation Note Not entirely consistent: We’ll use: input = x; output=y; pair = (x,y) Consistent with Berger, 1996 Ratnaparkhi, 1996: input = h; output=t; pair = (h,t) Klein/Manning, ‘03: input = d; output=c; pair = (c,d) 80

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. 81

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. Different types of models: Joint models (aka generative models) estimate P(x,y) by maximizing P(X,Y|Θ) 82

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. Different types of models: Joint models (aka generative models) estimate P(x,y) by maximizing P(X,Y|Θ) Most models so far: n-gram, Naïve Bayes, HMM, etc 83

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. Different types of models: Joint models (aka generative models) estimate P(x,y) by maximizing P(X,Y|Θ) Most models so far: n-gram, Naïve Bayes, HMM, etc Conceptually easy to compute weights: relative frequency 84

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. Different types of models: Joint models (aka generative models) estimate P(x,y) by maximizing P(X,Y|Θ) Most models so far: n-gram, Naïve Bayes, HMM, etc Conceptually easy to compute weights: relative frequency Conditional (aka discriminative) models estimate P(y|x), by maximizing P(Y|X, Θ) Models going forward: MaxEnt, SVM, CRF, … 85

Joint vs Conditional Models Assuming some training data {(x,y)}, need to learn a model Θ s.t. given a new x, can predict label y. Different types of models: Joint models (aka generative models) estimate P(x,y) by maximizing P(X,Y|Θ) Most models so far: n-gram, Naïve Bayes, HMM, etc Conceptually easy to compute weights: relative frequency Conditional (aka discriminative) models estimate P(y|x), by maximizing P(Y|X, Θ) Models going forward: MaxEnt, SVM, CRF, … Computing weights more complex 86

Naïve Bayes Model Naïve Bayes Model assumes features f are independent of each other, given the class C c f1f1 f2f2 f3f3 fkfk

Naïve Bayes Model Makes assumption of conditional independence of features given class 88

Naïve Bayes Model Makes assumption of conditional independence of features given class However, this is generally unrealistic 89

Naïve Bayes Model Makes assumption of conditional independence of features given class However, this is generally unrealistic P(“cuts”|politics) = p cuts 90

Naïve Bayes Model Makes assumption of conditional independence of features given class However, this is generally unrealistic P(“cuts”|politics) = p cuts What about P(“cuts”|politics,”budget”) ?= p cuts 91

Naïve Bayes Model Makes assumption of conditional independence of features given class However, this is generally unrealistic P(“cuts”|politics) = p cuts What about P(“cuts”|politics,”budget”) ?= p cuts Would like a model that doesn’t assume 92

Model Parameters Our model: c * = argmax c P(c)Π j P(f j |c) Types of parameters Two: P(C): Class priors P(f j |c): Class conditional feature probabilities Features in total |C|+|VC|, if features are words in vocabulary V

Weights in Naïve Bayes c1c1 c2c2 c3c3 …ckck f1f1 P(f 1 |c 1 )P(f 1 |c 2 )P(f 1 |c 3 )P(f 1 |c k ) f2f2 P(f 2 |c 1 )P(f 2 |c 2 )… …… f |V| P(f |V| |,c 1 ) 94

Weights in Naïve Bayes and Maximum Entropy Naïve Bayes: P(f|y) are probabilities in [0,1], weights 95

Weights in Naïve Bayes and Maximum Entropy Naïve Bayes: P(f|y) are probabilities in [0,1], weights P(y|x) = 96

Weights in Naïve Bayes and Maximum Entropy Naïve Bayes: P(f|y) are probabilities in [0,1], weights P(y|x) = MaxEnt: Weights are real numbers; any magnitude, sign 99

Weights in Naïve Bayes and Maximum Entropy Naïve Bayes: P(f|y) are probabilities in [0,1], weights P(y|x) = MaxEnt: Weights are real numbers; any magnitude, sign P(y|x) = 100

MaxEnt Overview Prediction: P(y|x) 101

MaxEnt Overview Prediction: P(y|x) f j (x,y): binary feature function, indicating presence of feature in instance x of class y 102

MaxEnt Overview Prediction: P(y|x) f j (x,y): binary feature function, indicating presence of feature in instance x of class y λ j : feature weights, learned in training 103

MaxEnt Overview Prediction: P(y|x) f j (x,y): binary feature function, indicating presence of feature in instance x of class y λ j : feature weights, learned in training Prediction: Compute P(y|x), pick highest y 104

Weights in MaxEnt c1c1 c2c2 c3c3 …ckck f1f1 λ1λ1 λ8λ8 … f2f2 λ2λ2 … …… f |V| λ6λ6 105

Maximum Entropy Principle 106

Maximum Entropy Principle Intuitively, model all that is known, and assume as little as possible about what is unknown 107

Maximum Entropy Principle Intuitively, model all that is known, and assume as little as possible about what is unknown Maximum entropy = minimum commitment 108

Maximum Entropy Principle Intuitively, model all that is known, and assume as little as possible about what is unknown Maximum entropy = minimum commitment Related to concepts like Occam’s razor 109

Maximum Entropy Principle Intuitively, model all that is known, and assume as little as possible about what is unknown Maximum entropy = minimum commitment Related to concepts like Occam’s razor Laplace’s “Principle of Insufficient Reason”: When one has no information to distinguish between the probability of two events, the best strategy is to consider them equally likely 110

Example I: (K&M 2003) Consider a coin flip H(X) 111

Example I: (K&M 2003) Consider a coin flip H(X) What values of P(X=H), P(X=T) maximize H(X)? 112

Example I: (K&M 2003) Consider a coin flip H(X) What values of P(X=H), P(X=T) maximize H(X)? P(X=H)=P(X=T)=1/2 If no prior information, best guess is fair coin 113

Example I: (K&M 2003) Consider a coin flip H(X) What values of P(X=H), P(X=T) maximize H(X)? P(X=H)=P(X=T)=1/2 If no prior information, best guess is fair coin What if you know P(X=H) =0.3? 114

Example I: (K&M 2003) Consider a coin flip H(X) What values of P(X=H), P(X=T) maximize H(X)? P(X=H)=P(X=T)=1/2 If no prior information, best guess is fair coin What if you know P(X=H) =0.3? P(X=T)=0.7 115

Example II: MT (Berger, 1996) Task: English  French machine translation Specifically, translating ‘in’ Suppose we’ve seen in translated as: {dans, en, à, au cours de, pendant} Constraint: 116

Example II: MT (Berger, 1996) Task: English  French machine translation Specifically, translating ‘in’ Suppose we’ve seen in translated as: {dans, en, à, au cours de, pendant} Constraint: p(dans)+p(en)+p(à)+p(au cours de)+p(pendant)=1 If no other constraint, what is maxent model? 117

Example II: MT (Berger, 1996) Task: English  French machine translation Specifically, translating ‘in’ Suppose we’ve seen in translated as: {dans, en, à, au cours de, pendant} Constraint: p(dans)+p(en)+p(à)+p(au cours de)+p(pendant)=1 If no other constraint, what is maxent model? p(dans)=p(en)=p(à)=p(au cours de)=p(pendant)=1/5 118

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint 119

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint: p(dans)+p(en)=3/10 Now what is maxent model? 120

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint: p(dans)+p(en)=3/10 Now what is maxent model? p(dans)=p(en)= 121

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint: p(dans)+p(en)=3/10 Now what is maxent model? p(dans)=p(en)=3/20 p(à)=p(au cours de)=p(pendant)= 122

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint: p(dans)+p(en)=3/10 Now what is maxent model? p(dans)=p(en)=3/20 p(à)=p(au cours de)=p(pendant)=7/30 What if we also know translate picks à or dans 50%? Add new constraint: p(à)+p(dans)=0.5 Now what is maxent model?? 123

Example II: MT (Berger, 1996) What we find out that translator uses dans or en 30%? Constraint: p(dans)+p(en)=3/10 Now what is maxent model? p(dans)=p(en)=3/20 p(à)=p(au cours de)=p(pendant)=7/30 What if we also know translate picks à or dans 50%? Add new constraint: p(à)+p(dans)=0.5 Now what is maxent model?? Not intuitively obvious… 124

Example III: POS (K&M, 2003) 125

Example III Problem: Too uniform What else do we know? Nouns more common than verbs 129

Example III Problem: Too uniform What else do we know? Nouns more common than verbs So f N ={NN,NNS,NNP,NNPS}, and E[f N ]=32/36 Also, proper nouns more frequent than common, so E[NNP,NNPS]=24/36 Etc 130

Feature Selection & Maximum Entropy Advanced Statistical Methods in NLP Ling 572 January 26, 2012 1.

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