Social Media & Text Analysis

Transcription

1 Social Media & Text Analysis lecture 5 - Paraphrase Identification and Logistic Regression CSE Ohio State University Instructor: Wei Xu Website: socialmedia-class.org

2 In-class Presentation pick your topic and sign up a 10 minute presentation (20 points) - A Social Media Platform - Or a NLP Researcher

3 Reading #6 & Quiz #3 socialmedia-class.org

4 Mini Research Proposal propose/explore NLP problems in GitHub dataset

5 Mini Research Proposal propose/explore NLP problems in GitHub dataset GitHub: - a social network for programmers (sharing, collaboration, bug tracking, etc.) - hosting Git repositories (a version control system that tracks changes to files, usually source code) - containing potentially interesting text fields in natural language (comments, issues, etc.)

6 (Recap) what is Paraphrase? sentences or phrases that convey approximately the same meaning using different words (Bhagat & Hovy, 2012)

7 (Recap) what is Paraphrase? sentences or phrases that convey approximately the same meaning using different words (Bhagat & Hovy, 2012) wealthy word rich

8 (Recap) what is Paraphrase? sentences or phrases that convey approximately the same meaning using different words (Bhagat & Hovy, 2012) wealthy the king s speech word phrase rich His Majesty s address

9 (Recap) what is Paraphrase? sentences or phrases that convey approximately the same meaning using different words (Bhagat & Hovy, 2012) wealthy the king s speech the forced resignation of the CEO of Boeing, Harry Stonecipher, for word phrase sentence rich His Majesty s address after Boeing Co. Chief Executive Harry Stonecipher was ousted from

10 The Ideal

11 (Recap) Paraphrase Research 80s WordNet 01 Web '01 Novels '04 News '05 '13 Bi-Text '11 Video '12* Style '13* '14* Twitter '15*'16* Simple Xu Ritter Dolan Grishman Cherry Xu Ritter Callison-Burch Dolan Ji Xu Callison-Burch Napoles * my research Wei Xu. Data-driven Approaches for Paraphrasing Across Language Variations PhD Thesis (2014)

12 DIRT (Discovery of Inference Rules from Text) Lin and Panel (2001) operationalize the Distributional Hypothesis using dependency relationships to define similar environments. Duty and responsibility share a similar set of dependency contexts in large volumes of text: modified by adjectives additional, administrative, assigned, assumed, collective, congressional, constitutional... objects of verbs assert, assign, assume, attend to, avoid, become, breach... Decking Lin and Patrick Pantel. DIRT - Discovery of Inference Rules from Text In KDD (2001)

13 Bilingual Pivoting word alignment... 5 farmers were thrown into jail in Ireland fünf Landwirte festgenommen, weil oder wurden festgenommen, gefoltert or have been imprisoned, tortured... Source: Chris Callison-Burch

14 Bilingual Pivoting word alignment... 5 farmers were thrown into jail in Ireland fünf Landwirte festgenommen, weil oder wurden festgenommen, gefoltert or have been imprisoned, tortured... Source: Chris Callison-Burch

15 Bilingual Pivoting word alignment... 5 farmers were thrown into jail in Ireland fünf Landwirte festgenommen, weil oder wurden festgenommen, gefoltert or have been imprisoned, tortured... Source: Chris Callison-Burch

16 Bilingual Pivoting word alignment... 5 farmers were thrown into jail in Ireland fünf Landwirte festgenommen, weil oder wurden festgenommen, gefoltert or have been imprisoned, tortured... Source: Chris Callison-Burch

17 Bilingual Pivoting word alignment... 5 farmers were thrown into jail in Ireland fünf Landwirte festgenommen, weil oder wurden festgenommen, gefoltert or have been imprisoned, tortured... Source: Chris Callison-Burch

18 Quiz #2 Key Limitations of PPDB?

19 Quiz #2 word sense microbe, virus, bacterium, germ, parasite insect, beetle, pest, mosquito, fly bother, annoy, pester bug microphone, tracker, mic, wire, earpiece, cookie glitch, error, malfunction, fault, failure squealer, snitch, rat, mole Source: Chris Callison-Burch

20 Another Key Limitation 80s WordNet 01 Web '01 Novels '04 News '05 '13 Bi-Text '11 Video '12* Style '13* '14* Twitter '15*'16* Simple only paraphrases, no non-paraphrases * my research Wei Xu. Data-driven Approaches for Paraphrasing Across Language Variations PhD Thesis (2014)

21 Paraphrase Identification obtain sentential paraphrases automatically Mancini has been sacked by Manchester City Mancini gets the boot from Man City Yes!% WORLD OF JENKS IS ON AT 11 No!$ World of Jenks is my favorite show on tv (meaningful) non-paraphrases are needed to train classifiers! Wei Xu, Alan Ritter, Chris Callison-Burch, Bill Dolan, Yangfeng Ji. Extracting Lexically Divergent Paraphrases from Twitter In

22 Also Non-Paraphrases 80s WordNet 01 Web '01 Novels '04 News '05 '13 Bi-Text '11 Video '12* Style '13* '14* Twitter '15*'16* Simple (meaningful) non-paraphrases are needed to train classifiers! * my research Wei Xu. Data-driven Approaches for Paraphrasing Across Language Variations PhD Thesis (2014)

23 News Paraphrase Corpus Microsoft Research Paraphrase Corpus also contains some non-paraphrases (Dolan, Quirk and Brockett, 2004; Dolan and Brockett, 2005; Brockett and Dolan, 2005)

24 Twitter Paraphrase Corpus also contains a lot of non-paraphrases Wei Xu, Alan Ritter, Chris Callison-Burch, Bill Dolan, Yangfeng Ji. Extracting Lexically Divergent Paraphrases from Twitter In TACL (2014)

25 Paraphrase Identification: A Binary Classification Problem Input: - a sentence pair x - a fixed set of binary classes Y = {0, 1} Output: - a predicted class y Y (y = 0 or y = 1)

26 Paraphrase Identification: A Binary Classification Problem Input: - a sentence pair x negative (non-paraphrases) - a fixed set of binary classes Y = {0, 1} Output: - a predicted class y Y (y = 0 or y = 1)

27 Paraphrase Identification: A Binary Classification Problem Input: - a sentence pair x negative (non-paraphrases) - a fixed set of binary classes Y = {0, 1} positive (paraphrases) Output: - a predicted class y Y (y = 0 or y = 1)

28 Paraphrase Identification: A Binary Classification Problem Input: - a sentence pair x - a fixed set of binary classes Y = {0, 1} Output: - a predicted class y Y (y = 0 or y = 1)

29 Classification Method: Supervised Machine Learning Input: - a sentence pair x - a fixed set of binary classes Y = {0, 1} - a training set of m hand-labeled sentence pairs (x (1), y (1) ),, (x (m), y (m) ) Output: - a learned classifier γ: x y Y (y = 0 or y = 1)

30 Classification Method: Supervised Machine Learning Input: - a sentence pair x (represented by features) - a fixed set of binary classes Y = {0, 1} - a training set of m hand-labeled sentence pairs (x (1), y (1) ),, (x (m), y (m) ) Output: - a learned classifier γ: x y Y (y = 0 or y = 1)

31 (Recap Week #3) Classification Method: Supervised Machine Learning Naïve Bayes Logistic Regression Support Vector Machines (SVM)

32 (Recap Week#3) Cons: Naïve Bayes features ti are assumed independent given the class y P(t 1,t 2,...,t n y) = P(t 1 y) P(t 2 y)... P(t n y) This will cause problems: - correlated features double-counted evidence - while parameters are estimated independently - hurt classier s accuracy

33 Classification Method: Supervised Machine Learning Naïve Bayes Logistic Regression Support Vector Machines (SVM)

34 Logistic Regression One of the most useful supervised machine learning algorithm for classification! Generally high performance for a lot of problems. Much more robust than Naïve Bayes (better performance on various datasets).

35 Before Logistic Regression Let s start with something simpler!

36 Paraphrase Identification: Simplified Features We use only one feature: - number of words that two sentence shared in common

37 A very related problem of Paraphrase Identification: Semantic Textual Similarity How similar (close in meaning) two sentences are? 5: completely equivalent in meaning 4: mostly equivalent, but some unimportant details differ 3: roughly equivalent, some important information differs/missing 2: not equivalent, but share some details 1: not equivalent, but are on the same topic 0: completely dissimilar

38 A Simpler Model: Linear Regression 5 Sentence Similarity (rated by Human) #words in common (feature)

39 A Simpler Model: Linear Regression 5 Sentence Similarity #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

40 A Simpler Model: Linear Regression 5 Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

41 A Simpler Model: Linear Regression paraphrase{ Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

42 A Simpler Model: Linear Regression 5 { paraphrase { non-paraphrase Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

43 Training Set #words in common (x) Sentence Similarity (y) m hand-labeled sentence pairs (x (1), y (1) ),, (x (m), y (m) ) - x s: input variable / features - y s: output / target variable

44 (Recap Week#3) Supervised Machine Learning training set Source: NLTK Book

45 Supervised Machine Learning training set (also called) hypothesis

46 Supervised Machine Learning training set #words in common Sentence Similarity (also called) hypothesis

47 Supervised Machine Learning training set x (estimated) y #words in common Sentence Similarity (also called) hypothesis

48 Linear Regression: Model Representation How to represent h? training set h θ (x) = θ 0 +θ 1 x x (estimated) y Linear Regression w/ one variable hypothesis (h)

49 Linear Regression w/ one variable: Model Representation #words in common (x) Sentence Similarity (y) h θ (x) = θ 0 +θ 1 x m hand-labeled sentence pairs (x (1), y (1) ),, (x (m), y (m) ) θ s: parameters Source: many following slides are adapted from Andrew Ng

50 Linear Regression w/ one variable: Model Representation #words in common (x) Sentence Similarity (y) h θ (x) = θ 0 +θ 1 x m hand-labeled sentence pairs (x (1), y (1) ),, (x (m), y (m) ) - θ s: parameters How to choose θ?

51 Linear Regression w/ one variable:: Model Representation 3' 3' 3' 2' 2' 2' 1' 1' 1' 0' 0' 1' 2' 3' 0' 0' 1' 2' 3' 0' 0' 1' 2' 3' Andrew'Ng'

52 Linear Regression w/ one variable: Cost Function 5 Sentence Similarity (rated by Human) #words in common (feature) Idea: choose θ0, θ1 so that hθ(x) is close to y for training examples (x, y)

53 Linear Regression w/ one variable: Cost Function 5 Sentence Similarity (rated by Human) #words in common (feature) Idea: choose θ0, θ1 so that hθ(x) is close to y for training examples (x, y)

54 Linear Regression w/ one variable: Cost Function 5 Sentence Similarity (rated by Human) #words in common (feature) squared error function: J(θ 0,θ 1 ) = 1 m 2m (h θ (x (i) ) y (i) ) 2 i=1 Idea: choose θ0, θ1 so that hθ(x) is close to y for training examples (x, y) minimize θ 0, θ 1 J(θ 0,θ 1 )

55 Linear Regression Hypothesis: h θ (x) = θ 0 +θ 1 x Parameters: θ0, θ1 Cost Function: J(θ 0,θ 1 ) = 1 Goal: ' m 2m (h θ (x(i) ) y (i) ) 2 i=1 minimize θ 0, θ 1 J(θ 0,θ 1 )

56 Hypothesis: Linear Regression Simplified h θ (x) = θ 0 +θ 1 x h θ (x) = θ 1 x Parameters: θ0, θ1 θ1 Cost Function: J(θ 0,θ 1 ) = 1 Goal: ' m 2m (h θ (x(i) ) y (i) ) 2 i=1 minimize θ 0, θ 1 J(θ 0,θ 1 ) J(θ 1 ) = 1 m 2m (h θ (x (i) ) y (i) ) 2 i=1 minimize θ 1 J(θ 1 )

57 h θ (x) (for fixed θ1, this is a function of x ) J(θ 1 ) (function of the parameter θ1 ) 3 3 y 2 1 θ1=1 J(θ1) x J(1) = [(1 1)2 + (2 2) 2 + (3 3) 2 ] = θ1

58 h θ (x) (for fixed θ1, this is a function of x ) J(θ 1 ) (function of the parameter θ1 ) 3 3 y 2 1 θ1=0.5 J(θ1) x J(1) = [(0.5 1)2 + (1 2) 2 + (1.5 3) 2 ] = θ1

59 h θ (x) (for fixed θ1, this is a function of x ) J(θ 1 ) (function of the parameter θ1 ) 3 3 y 2 1 J(θ1) x θ1 minimize θ 1 J(θ 1 )

60 h θ (x) J(θ 0,θ 1 ) (for fixed θ0, θ1, this is a function of x ) (function of the parameter θ0, θ1 ) J(θ1, θ2) θ1 θ0

61 Hypothesis: Linear Regression Simplified h θ (x) = θ 0 +θ 1 x h θ (x) = θ 1 x Parameters: θ0, θ1 θ1 Cost Function: J(θ 0,θ 1 ) = 1 Goal: ' m 2m (h θ (x(i) ) y (i) ) 2 i=1 minimize θ 0, θ 1 J(θ 0,θ 1 ) J(θ 1 ) = 1 m 2m (h θ (x (i) ) y (i) ) 2 i=1 minimize θ 1 J(θ 1 )

62 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) contour plot Andrew'Ng'

63 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

64 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

65 Parameter Learning Have some function J(θ 0,θ 1 ) Want min J(θ,θ ) 0 1 θ 0, θ 1 Outline: - Start with some θ0, θ1 - Keep changing θ0, θ1 to reduce J(θ1, θ2) until we hopefully end up at a minimum

66 Gradient Descent 3 J(θ1) θ1 minimize θ 1 J(θ 1 )

67 Gradient Descent 3 J(θ1) 2 1 θ 1 := θ 1 α θ 1 J(θ 1 ) θ1 learning rate minimize θ 1 J(θ 1 )

68 Gradient Descent J(θ 0,θ 1 ) " θ 1" θ 0" minimize θ 0,θ 1 J(θ 0,θ 1 ) Andrew'Ng'

69 Gradient Descent J(θ 0,θ 1 ) " θ 0" θ 1" minimize θ 0,θ 1 J(θ 0,θ 1 ) Andrew'Ng'

70 Gradient Descent repeat until convergence { θ j := θ j α θ j J(θ 0,θ 1 ) (simultaneous update for j=0 and j=1) } learning rate

71 Linear Regression w/ one variable: Gradient Descent Cost Function h θ (x) = θ 0 +θ 1 x J(θ 0,θ 1 ) = 1 m 2m (h θ (x(i) ) y (i) ) 2 i=1 θ 0 J(θ 0,θ 1 ) =? θ 1 J(θ 0,θ 1 ) =?

72 Linear Regression w/ one variable: Gradient Descent repeat until convergence { } m simultaneous θ 0 := θ 0 α 1 m (h θ (x (i) ) y (i) ) i=1 m i=1 θ := θ α m (h (x (i) ) y (i) ) x (i) θ update θ0, θ1

73 Linear Regression cost function is convex J(θ1, θ2) θ1 θ0

74 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

75 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

76 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

77 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

78 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

79 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

80 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

81 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

82 h θ (x) (for fixed θ0, θ1, this is a function of x ) J(θ 0,θ 1 ) (function of the parameter θ0, θ1 ) Andrew'Ng'

83 Batch Update Each step of gradient descent uses all the training examples

84 (Recap) Linear Regression 5 Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

85 (Recap) Linear Regression paraphrase{ Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

86 (Recap) Linear Regression 5 { paraphrase { non-paraphrase Sentence Similarity threshold Classification #words in common (feature) also supervised learning (learn from annotated data) but for Regression: predict real-valued output (Classification: predict discrete-valued output)

87 (Recap) Hypothesis: Linear Regression h θ (x) = θ 0 +θ 1 x Parameters: J(θ1, θ2) θ0, θ1 Cost Function: J(θ 0,θ 1 ) = 1 Goal: m 2m (h θ (x(i) ) y (i) ) 2 i=1 minimize θ 0, θ 1 J(θ 0,θ 1 ) θ1 θ0

88 (Recap) Gradient Descent repeat until convergence { θ j := θ j α θ j J(θ 0,θ 1 ) (simultaneous update for j=0 and j=1) } learning rate

89 Next Class: - Logistic Regression (cont ) socialmedia-class.org