linear programming in concept semantic networks by naser madi
TRANSCRIPT
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Text Comprehension
Comprehension is understanding letters and words, syntactic parsing of sentences, understanding the meaning of words and sentences [1]
Comprehension
SegmentationRecognizing ideas
IntegrationConnecting ideas tobackground knowledge
3
Text Comprehension
"Some kids found her upstairs"
"Hasn't been here long, her name's Jennifer Wilson according to her credit cards"
"We're running them now for contact details"
4
Segmentation & Integration thresholds
Concept recognition (segmentation) threshold is the individual limit for recognizing concepts [2]
Association recognition (integration) threshold is the individual limit for recognizing associations [2]
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Segmentation & Integration thresholds
a
b
c
d
e
f
e1=5
e2=5e3=3
e4=2
e5=3
e6=2
e7=5e
8=5
e9=3
a
b
c
d
f
a
b
c
d
f
Recognition threshold, α = 10 Association threshold, β= 4
Segmentation Integration
Currently recognized concepts
e1=5
e2=5e3=3
e7=5e
8=5e9=3
e1=5
e2=5
e7=5e
8=5
321 eee
987 eee
654 eee
1e 2e
7e8e
3e
9e
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Optimization
321 eee 654 eee 987 eee
01 e
02 e
03 e
07 e08 e09 e
Linear programmi
ng
,,...,, 321 eee
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Base Semantic Network I
a
b c
d
e
a
b
d
e
a
b c
d
e
a
b c
e
ISN (reader 1)
ISN (reader 2) ISN (reader
3)
BSN
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Mutual Exclusion
The problem of mutual exclusivity may arise between two individuals when given the same background knowledge two or more individuals recognize a different set of concepts
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Mutual Exclusion
The solution is to add a hidden node for each reader indicating the previous knowledge possessed by a reader [2]
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Base Semantic Network II
CXC + CXE + SXC + α+ β
CXC: associations between concepts CXE: concepts discovered at episode SXC: subject recognized concept α: recognition threshold β: association threshold
a
b c
d
e
E1 E2 E3
S1
S2
S3
S4
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Linear Programming
The matrix representation for the equations as a linear programming problem is as follows:
min ƒ*x subject to constraints Ax ≤ b
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Linear Programming
Each inequality is a line (half space)
Each variable is a dimension
If a solution is possible and the inequalities are Satisfiable, then the polygon covers the area of feasible solution [2]
Testing the corner values (intersection points) of the polygon gives us the min & max [4]
simple linear program with two variables and six inequalities
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Sample BSN
Weighted graph. 8534 variables. 87 concepts.
Contains individual association and recognition thresholds (α and β).
CXC + CXE + SXC + α+ β
7*7+7*3+2*7+2*2 = 98
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Linear Programming Example For example, we can maximize: F = 2 α + 3 β
Constraint by: 2 α + 4 β <= 12 α + β <= 4 α >=0 β >=0
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Complexity
In general the computational complexity of current interior point methods [5] is O(N3L) where N is the number of variables and L is the size of data (number of inequalities) [2]
Worst case of simplex method is exponential [4]
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References
[1] W. Kintsch, The construction-integration model of text comprehension and its implications for instruction," Theoretical models and processes of reading, vol. 5, pp. 1270{1328, 2004.
[2] M. Hardas and J. Khan, Concept learning in text comprehension," in Brain Informatics. Springer, 2010, pp. 240{251.
[3] Dantzig, G.B., A. Orden, and P. Wolfe, "Generalized Simplex Method for Minimizing a Linear Form Under Linear Inequality Restraints," Pacific Journal Math., Vol. 5, pp. 183–195, 1955.
[4] http://en.wikipedia.org/wiki/Linear_programming
[5] Khachiyan, Leonid G. "Polynomial algorithms in linear programming." USSR Computational Mathematics and Mathematical Physics 20.1 (1980): 53-72.