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Language Independent Methods of Clustering Contexts

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University of Minnesota, Duluth
University of Minnesota, Duluth
Tecnología
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Language Independent Methods of Clustering Similar Contexts (with applications) Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse [email_address]
The Problem ,[object Object],[object Object],[object Object],[object Object],[object Object]
Language Independent Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline (Tutorial) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline (Practical Session) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SenseClusters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Many thanks… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Practical Session ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Background and Motivations
Headed and Headless Contexts ,[object Object],[object Object],[object Object],[object Object]
Headed Contexts (input) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Headed Contexts (output) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Headless Contexts (input) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Headless Contexts (output) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
 
 
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
Applications ,[object Object],[object Object],[object Object],[object Object]
 
 
 
Underlying Premise… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Identifying Lexical Features Measures of Association and Tests of Significance
What are features? ,[object Object],[object Object],[object Object]
Where do features come from? ,[object Object],[object Object],[object Object],[object Object]
Feature Selection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Lexical Features ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bigrams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Co-occurrences ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bigrams and Co-occurrences ,[object Object],[object Object],[object Object],[object Object],[object Object]
“ occur together more often than expected by chance…” ,[object Object],[object Object],[object Object],[object Object],[object Object]
2x2 Contingency Table 100,000 300 !Artificial 400 100 Artificial !Intelligence Intelligence
2x2 Contingency Table 100,000 99,700 300 99,600 99,400 200 !Artificial 400 300 100 Artificial !Intelligence Intelligence
2x2 Contingency Table 100,000 99,700 300 99,600 99,400.0 99,301.2 200.0 298.8 !Artificial 400 300.0 398.8 100.0 000.12 Artificial !Intelligence Intelligence
Measures of Association
Measures of Association
Interpreting the Scores… ,[object Object],[object Object]
 
Interpreting the Scores… ,[object Object],[object Object],[object Object]
Measures of Association ,[object Object],[object Object],[object Object]
Measures Supported in NSP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
NSP ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Context Representations First and Second Order Methods
Once features selected… ,[object Object],[object Object],[object Object]
First Order Representation ,[object Object],[object Object],[object Object]
Contexts ,[object Object],[object Object],[object Object],[object Object]
Unigram Feature Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
First Order Vectors of Unigrams 1 0 1 0 1 C4 0 0 0 0 0 C3 1 1 0 1 0 C2 1 1 1 1 1 C1 child magic curse black island
Bigram Feature Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
First Order Vectors of Bigrams 1 0 1 1 0 C4 0 1 1 0 0 C3 1 0 0 0 1 C2 1 0 0 1 1 C1 voodoo child serious error military might island curse black magic
First Order Vectors ,[object Object],[object Object],[object Object],[object Object],[object Object]
Second Order Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Word by Word Matrix 120.0 0 69.4 0 0 voodoo 0 89.2 0 21.2 0 serious 0 54.9 100.3 0 0 military 73.2 0 0 189.2 0 island 43.2 0 0 0 123.5 black child error might curse magic
Word by Word Matrix ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
There was an island curse of black magic cast by that voodoo child. 120.0 0 69.4 0 0 voodoo 73.2 0 0 189.2 0 island 43.2 0 0 0 123.5 black child error might curse magic
Second Order Representation ,[object Object],[object Object]
There was an island curse of black magic cast by that voodoo child. 78.8 0 24.4 63.1 41.2 C1 child error might curse magic
First versus Second Order ,[object Object],[object Object],[object Object],[object Object]
Second Order Co-Occurrences ,[object Object],[object Object]
Second Order Co-occurrences ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dimensionality Reduction Singular Value Decomposition
Motivation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Many Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Effect of SVD ,[object Object],[object Object]
Effect of SVD ,[object Object],[object Object],[object Object]
How can SVD be used? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Word by Word Matrix 4 2 0 0 0 3 0 1 box 0 1 2 2 1 2 0 0 memory 0 0 0 1 0 0 2 0 organ 0 2 0 3 2 0 0 0 debt 0 1 0 3 1 0 0 2 linux 0 1 0 3 2 0 0 0 sales 3 0 2 2 0 3 0 0 lab 1 0 2 0 0 1 2 0 petri 0 1 0 0 2 0 0 1 disk 1 0 2 0 0 0 3 0 body 0 0 0 3 1 0 0 2 pc plasma graphics tissue data ibm cells blood apple
Singular Value Decomposition A=UDV’
U -.52 .39 -.48 .02 .09 .41 -.09 .40 -.30 .08 .31 .43 -.26 -.39 -.6 .20 .00 -.00 -.00 -.02 -.01 .00 -.02 -.00 -.07 -.3 .14 -.49 -.07 .30 .25 .56 -.01 .08 .05 -.01 .24 -.08 .11 .46 .08 .03 -.04 .72 .09 -.31 -.01 .37 -.07 .01 -.21 -.31 -.34 -.45 -.68 .29 .00 .05 .83 .17 -.02 .25 -.45 .08 .03 .20 -.22 .31 -.60 .39 .13 .35 -.01 -.04 -.44 .08 .44 .59 -.49 .05 -.02 .63 .02 -.09 .52 -.2 .09 .35
D 0.00 0.00 0.00 0.66 1.26 2.30 2.52 3.25 3.99 6.36 9.19
V -.20 .22 -.07 -.10 -.87 -.07 -.06 .17 .19 -.26 .04 .03 .17 -.32 .02 .13 -.26 -.17 .06 -.04 .86 .50 -.58 .12 .09 -.18 -.27 -.18 -.12 -.47 .11 -.03 .12 .31 -.32 -.04 .64 -.45 -.14 -.23 .28 .07 -.23 -.62 -.59 .05 .02 -.12 .15 .11 .25 -.71 -.31 -.04 .08 .29 -.05 .05 .20 -.51 .09 -.03 .12 .31 -.01 .02 -.45 -.32 .50 .27 .49 -.02 .08 .21 -.06 .08 -.09 .52 -.45 -.01 .63 .03 -.12 -.31 .71 -.13 .39 -.12 .12 .15 .37 .07 .58 -.41 .15 .17 -.30 -.32 -.27 -.39 .11 .44 .25 .03 -.02 .26 .23 .39 .57 -.37 .04 .03 -.12 -.31 -.05 -.05 .04 .28 -.04 .08 .21
Word by Word Matrix After SVD 1.1 1.0 .98 1.7 .86 .72 .85 .77 memory .00 .00 .17 1.2 .77 .00 .84 .00 organ .00 1.5 .00 3.2 2.1 .00 .00 1.2 debt .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .41 .85 .35 2.2 1.3 .39 .15 .73 sales 2.3 .18 2.5 1.7 .35 2.0 1.7 .21 lab 1.4 .00 1.5 .49 .00 1.2 1.1 .00 germ .00 .91 .00 2.1 1.3 .01 .00 .76 disk 1.5 .00 1.6 .33 .00 1.3 1.2 .00 body .09 .86 .01 2.0 1.3 .11 .00 .73 pc plasma graphics tissue data ibm cells blood apple
Second Order Representation ,[object Object],[object Object],[object Object],[object Object],1.0 .72 memory .00 .00 organ .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .00 .91 .00 2.1 1.3 .01 .00 .76 disk Plasma graphics tissue data ibm cells blood apple
Clustering Methods Agglomerative and Partitional
Many many methods… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General Methodology ,[object Object],[object Object],[object Object],[object Object]
Agglomerative Clustering ,[object Object],[object Object],[object Object]
Measuring Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agglomerative Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Average Link Clustering 1 2 4 S3 1 2 4 S3 0 2 S4 0 3 S2 2 3 S1 S4 S2 S1 0 S4 0 S2 S1S3 S4 S2 S1S3 S4 S1S3S2 S4 S1S3S2
Partitional Methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
Partitional Methods ,[object Object],[object Object]
Cluster Labeling
Results of Clustering ,[object Object],[object Object],[object Object],[object Object]
Label Types ,[object Object],[object Object]
Evaluation Techniques Comparison to gold standard data
Evaluation ,[object Object],[object Object],[object Object],[object Object]
Evaluation ,[object Object],[object Object],[object Object],[object Object]
Evaluation ,[object Object],[object Object],[object Object]
Baseline Algorithm ,[object Object],[object Object]
Baseline Performance ,[object Object],170 55 35 80 Totals 170 55 35 80 C3 0 0 0 0 C2 0 0 0 0 C1 Totals S3 S2 S1 170 80 35 55 Totals 170 80 35 55 C3 0 0 0 0 C2 0 0 0 0 C1 Totals S1 S2 S3
Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],170 55 35 80 Totals 65 10 5 50 C3 60 40 0 20 C2 45 5 30 10 C1 Totals S3 S2 S1
Evaluation ,[object Object],[object Object],[object Object],170 80 55 35 Totals 65 50 10 5 C3 60 20 40 0 C2 45 10 5 30 C1 Totals S1 S3 S2
Analysis ,[object Object],[object Object],[object Object],[object Object]
Practical Session Experiments with SenseClusters
Experimental Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Creating Experimental Data ,[object Object],[object Object],[object Object],[object Object],[object Object]
Name Conflation Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering Contexts ,[object Object],[object Object],[object Object],[object Object]
Name Discrimination
George Millers!
Headed Clustering ,[object Object],[object Object],[object Object]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Headless Contexts ,[object Object],[object Object]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
If you after all these matrices you crave knowledge based resources… Read on…
WordNet-Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Many thanks! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Vector measure ,[object Object],[object Object],[object Object],[object Object],[object Object]
Many other measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
Thank you! ,[object Object],[object Object],[object Object]

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Language Independent Methods of Clustering Contexts

  • 1. Language Independent Methods of Clustering Similar Contexts (with applications) Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse [email_address]
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  • 29. Identifying Lexical Features Measures of Association and Tests of Significance
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  • 38. 2x2 Contingency Table 100,000 300 !Artificial 400 100 Artificial !Intelligence Intelligence
  • 39. 2x2 Contingency Table 100,000 99,700 300 99,600 99,400 200 !Artificial 400 300 100 Artificial !Intelligence Intelligence
  • 40. 2x2 Contingency Table 100,000 99,700 300 99,600 99,400.0 99,301.2 200.0 298.8 !Artificial 400 300.0 398.8 100.0 000.12 Artificial !Intelligence Intelligence
  • 43.
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  • 45.
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  • 50.
  • 51. Context Representations First and Second Order Methods
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  • 56. First Order Vectors of Unigrams 1 0 1 0 1 C4 0 0 0 0 0 C3 1 1 0 1 0 C2 1 1 1 1 1 C1 child magic curse black island
  • 57.
  • 58. First Order Vectors of Bigrams 1 0 1 1 0 C4 0 1 1 0 0 C3 1 0 0 0 1 C2 1 0 0 1 1 C1 voodoo child serious error military might island curse black magic
  • 59.
  • 60.
  • 61. Word by Word Matrix 120.0 0 69.4 0 0 voodoo 0 89.2 0 21.2 0 serious 0 54.9 100.3 0 0 military 73.2 0 0 189.2 0 island 43.2 0 0 0 123.5 black child error might curse magic
  • 62.
  • 63. There was an island curse of black magic cast by that voodoo child. 120.0 0 69.4 0 0 voodoo 73.2 0 0 189.2 0 island 43.2 0 0 0 123.5 black child error might curse magic
  • 64.
  • 65. There was an island curse of black magic cast by that voodoo child. 78.8 0 24.4 63.1 41.2 C1 child error might curse magic
  • 66.
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  • 69.
  • 70.
  • 71. Dimensionality Reduction Singular Value Decomposition
  • 72.
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  • 76.
  • 77. Word by Word Matrix 4 2 0 0 0 3 0 1 box 0 1 2 2 1 2 0 0 memory 0 0 0 1 0 0 2 0 organ 0 2 0 3 2 0 0 0 debt 0 1 0 3 1 0 0 2 linux 0 1 0 3 2 0 0 0 sales 3 0 2 2 0 3 0 0 lab 1 0 2 0 0 1 2 0 petri 0 1 0 0 2 0 0 1 disk 1 0 2 0 0 0 3 0 body 0 0 0 3 1 0 0 2 pc plasma graphics tissue data ibm cells blood apple
  • 79. U -.52 .39 -.48 .02 .09 .41 -.09 .40 -.30 .08 .31 .43 -.26 -.39 -.6 .20 .00 -.00 -.00 -.02 -.01 .00 -.02 -.00 -.07 -.3 .14 -.49 -.07 .30 .25 .56 -.01 .08 .05 -.01 .24 -.08 .11 .46 .08 .03 -.04 .72 .09 -.31 -.01 .37 -.07 .01 -.21 -.31 -.34 -.45 -.68 .29 .00 .05 .83 .17 -.02 .25 -.45 .08 .03 .20 -.22 .31 -.60 .39 .13 .35 -.01 -.04 -.44 .08 .44 .59 -.49 .05 -.02 .63 .02 -.09 .52 -.2 .09 .35
  • 80. D 0.00 0.00 0.00 0.66 1.26 2.30 2.52 3.25 3.99 6.36 9.19
  • 81. V -.20 .22 -.07 -.10 -.87 -.07 -.06 .17 .19 -.26 .04 .03 .17 -.32 .02 .13 -.26 -.17 .06 -.04 .86 .50 -.58 .12 .09 -.18 -.27 -.18 -.12 -.47 .11 -.03 .12 .31 -.32 -.04 .64 -.45 -.14 -.23 .28 .07 -.23 -.62 -.59 .05 .02 -.12 .15 .11 .25 -.71 -.31 -.04 .08 .29 -.05 .05 .20 -.51 .09 -.03 .12 .31 -.01 .02 -.45 -.32 .50 .27 .49 -.02 .08 .21 -.06 .08 -.09 .52 -.45 -.01 .63 .03 -.12 -.31 .71 -.13 .39 -.12 .12 .15 .37 .07 .58 -.41 .15 .17 -.30 -.32 -.27 -.39 .11 .44 .25 .03 -.02 .26 .23 .39 .57 -.37 .04 .03 -.12 -.31 -.05 -.05 .04 .28 -.04 .08 .21
  • 82. Word by Word Matrix After SVD 1.1 1.0 .98 1.7 .86 .72 .85 .77 memory .00 .00 .17 1.2 .77 .00 .84 .00 organ .00 1.5 .00 3.2 2.1 .00 .00 1.2 debt .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .41 .85 .35 2.2 1.3 .39 .15 .73 sales 2.3 .18 2.5 1.7 .35 2.0 1.7 .21 lab 1.4 .00 1.5 .49 .00 1.2 1.1 .00 germ .00 .91 .00 2.1 1.3 .01 .00 .76 disk 1.5 .00 1.6 .33 .00 1.3 1.2 .00 body .09 .86 .01 2.0 1.3 .11 .00 .73 pc plasma graphics tissue data ibm cells blood apple
  • 83.
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  • 89.
  • 90. Average Link Clustering 1 2 4 S3 1 2 4 S3 0 2 S4 0 3 S2 2 3 S1 S4 S2 S1 0 S4 0 S2 S1S3 S4 S2 S1S3 S4 S1S3S2 S4 S1S3S2
  • 91.
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  • 96. Evaluation Techniques Comparison to gold standard data
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  • 105. Practical Session Experiments with SenseClusters
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  • 157. If you after all these matrices you crave knowledge based resources… Read on…
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