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V= {a,b,c,d,e}

E= {(a,b),(a,c),(a,d),(b,e),(c,d),(c,e),

(d,e)}



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– , 6% 7 6 7

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0

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,- & 1

$# $

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connected not connected

• connected graph: any two vertices are connected by some path

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/ 2

( % 6% 7 4 7% 6

( % 8 6% 79 ?4 8 7% 69

tail head



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* 2)

1 -

weighted unweighted

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345- ., 6(

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, 3 * $ % 3@

% 3 1% 3@ABA3B47

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undirected: n2 /2

directed: n2

Examples of Adjacency Matrices

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1 3 *

• ( 49 <

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87135-

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←←←← "

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HLT/NAACL 2006 25

7 ) / 7 1

E

F

G

B

CD

A start

destination

A DFS on A A

DFS on BBA

DFS on C

B

C

AB Return to call on B

D Call DFS on D

A

BD

Call DFS on GG found destination - done

path is implicitly stored in DFS recursionpath is: A, B, D, G

HLT/NAACL 2006 26

E

F

G

B

CD

Astart

destination

A

Initial call to BFS on A

Add A to queue

B

Dequeue A

Add B

frontrear frontrear

C

Dequeue B

Add C, D

frontrear

D D

Dequeue C

Nothing to add

frontrear

G

Dequeue D

Add G

frontrear

found destination - donepath must be stored separately

7 ) 8 7 1



HLT/NAACL 2006 27

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HLT/NAACL 2006 28

6 1

( % 45%1

– $

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45%1%)% $

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HLT/NAACL 2006 29

= 6 1

$ * 0 $

–

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$

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5 3 #

– , # %

– : # % $ 3 * %

– D #

HLT/NAACL 2006 30

=

( !,

+

$ *

3

)

$ * *

C

3



HLT/NAACL 2006 31

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v1 v2

v3 v4 v5

v6 v7

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HLT/NAACL 2006 32

=

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v3 v4 v5

v6 v7

2

14

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6 1

/# – $ % 45%1%)% , $

& ,#

,%

*

– , ,

(

# % 4 5% 1% )% ,7 ,< ,%

, $ F5F7

HLT/NAACL 2006 34

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HLT/NAACL 2006 35

7) )>

) * G

1*# – ! ' &&% %

1 E % E 3

– / ' 1 E ' % E 3

. # – *

– * – : E

–

HLT/NAACL 2006 36

7;

: ' E

– /% ≥ 6

– , #

– : * →→→→

: E # × →

– #∀ ∈ # ≤

– # ∀ ∈∈∈∈ # 4 E – ∀ ∈∈∈∈ E H I#

∈

∈

v V

v V

, o f(u,v)= f(u,V)= 0

f(v,u)= f(V

r

,u)=0



HLT/NAACL 2006 37

)

/ $ $ G

? $ &

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11

54

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a b

c d

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HLT/NAACL 2006 38

6( )

) *+

#∈

v V

f = f(s,v)= f(s,V)= f(V,t)

Find a flow of maximum value.

13

11

54

15

10

14

19

3

s t 9

a b

c d

5

13

3

8

6

10

8

2



HLT/NAACL 2006 39

# – %

! 9 6% # = ≤≤≤≤

– ' % $

( G

13

11

54

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14

19

3

s t 9

a b

c d

5

13

3

8

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108

2

HLT/NAACL 2006 40

77>

Ford-Fulkerson(G,s,t)

1 initialize flow f to 0 everywhere

2 while there is an augmenting path p do

3 augment flow f along p

4 return f



HLT/NAACL 2006 41

)>

J G

" #

– # 4 E

– '# 4 % 4 H ∈∈∈∈ ×××× # 9 6I

Compute residual network:

13

11

54

15

10

14

19

3

s t 9

a b

c d

5

13

3

8

6

108

2

HLT/NAACL 2006 42

,

J G

" #

4 H # ∈ I

/ #

– ∀ % $

– &

13

11

54

15

10

14

19

3

s t 9

a b

c d

5

13

3

8

6

108

2What is the residual capacity of the

path (s,a,b,t)?



HLT/NAACL 2006 43

77> & )

Ford-Fulkerson(G,s,t)

1 for each edge (u,v)∈G.E do

2 f(u,v) = f(v,u) = 0

3 while ∃ path p from s to t in residual network Gfdo

4 cf= min{c

f(u,v): (u,v)∈p}

5 for each edge (u,v) in p do

6 f(u,v) = f(u,v) + cf

7 f(v,u) = -f(u,v)

8 return f

, $ >&

HLT/NAACL 2006 44

77> & (

13

11

54

15

10

14

19

3

s t

a b

c d

3

3

3

13

11

54

15

10

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19

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s t

a b

c d

13

3

3

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54

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s t

a b

c d

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3

8

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5



HLT/NAACL 2006 45

( % , ∈

% ∈

,& – , % , #

# &

∈∈∈∈∈∈∈∈∈∈∈∈

====

T w S v

E w v S e

w v u e u

,),(ofout

).,(:)(

s

2

3

4

5

6

7

t

15

5

30

15

10

8

15

9

6 10

10

10154

4

Capacity = 30

HLT/NAACL 2006 46

s

2

3

4

5

6

7

t

15

5

30

15

10

8

15

9

6 10

10

10154

4

Capacity = 62

s

2

3

4

5

6

7

t

15

5

30

15

10

8

15

9

6 10

10

10154

4

Capacity = 28



HLT/NAACL 2006 47

7)

10

6

6

10

10

0 10

4 8 8

0

4 0

00

s

2

3

4

5

6

7

t

15

5

30

15

10

8

15

9

6 10

10

10154

4

Value = 24

7& $ % % , $ & ,%

; &

f e f e f e f

s e S e S e

========−−−− ofouttoinofout

)()()(

HLT/NAACL 2006 48

6( 7) 6

.& $ % % , $ & FF 4% ,% * % , &

* - ::'# '% * ; &

s

2

3

4

5

6

7

t

15

5

30

15

10

8

15

9

6 10

10

10154

4

10

9

9

15

15

4 10

4 8 9

10 0

Flow value = 28

00

Cut capacity = 28



HLT/NAACL 2006 49

'

-*

HLT/NAACL 2006 50

, 45%1 J4)%: $3 # 5 → ) % 5# – HI ∈1 ⇔ H%I ∈ :

! "#

! $ → → % !

!&" $ → – " $ ' ( ∈ ⇔' ( ∈

– " $ " %



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1

( 45%1 $ J4)%: $ J7 J J7

HLT/NAACL 2006 52

?(

F5F ≠ F)F% F1F ≠ F:F% # & /&

"% J %

– 1 J

– J% $ % &

: # $ % &

– % ;

* &



HLT/NAACL 2006 53

1

; 4 ( 7

$ ( 7

45% ; J 4 07 $ J 074)&

– ( $ ( 7%K%( 0 J $ 0 7%K%0&

( 7%K%( (

0 7%K%0 0

: # ( # 0#

– . * ( 0 $ ( # 0 # % # &

– ( 4 H ( F $ ( # I

– 0 4 H 0 F $ 0 # I

– F( F ≠ F0 F% #

HLT/NAACL 2006 54

,>& &



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. J

1

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HLT/NAACL 2006 56

,

. * & $ $

, .5#

– / # $ 5L $

– / # 5

– 0 # 5

– 1 # ' ' 5



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$ &

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( M N# % #

– 1 ' ' – " 3

HLT/NAACL 2006 58

)>

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) $& O $ 6G

J #

– !6 4 6 – ! 4 7

– !* 4 PQ*7= PQ*=7% 68*8

– % P $ '

0 1 2 3 4 5



HLT/NAACL 2006 59

/

/$ & " 46%

# 47&

L ;#

, ; #

% ; &

02=+=∇ yy xx uuu

t uuk =∇2

(**)

HLT/NAACL 2006 60

, *

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– (3 $ $&

– &

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0

( MN *

+% $ $ * $

. #

0)det( =− I A λ

vv A

λ =

HLT/NAACL 2006 62

0

1*#

/ (λ 4 7λQλ>Q<46

,# λ=λ<R46C λ74<C λ<4>

: λ1=2:

# *74*<

−=

02

31 A

−

−−=−

λ

λ λ

2

31 I A

022

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=

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x

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HLT/NAACL 2006 63

1

# 7 6& 1*#

, * # $ 7 4 7&

: $ 7% % 4

% ,

4 &

=

43

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85

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A

HLT/NAACL 2006 64

0 )> )>

= y

xy xC C

1 ' * %

11

1 0.5

0.5a b

c

d

xy

xy R

C 1

=

x

xy

xyC

C P =

05

2

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100

2

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d cba

a

b

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314

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From Doyle and Snell 2000



HLT/NAACL 2006 65

0 )> )>

xy y x

xy

y x

xy C vv R

vvi )( −=

−

=

y

y

xy y

y x

xy

x vPvc

cv ==

11

1 0.5

0.5a

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1 V

b

= y xyi 0

0

1

=

=

b

a

v

v

$$ '

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8

3

5

2

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116

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HLT/NAACL 2006 66

0,

,

" %

, * #

.

yx xy f f −=

xy xy Ri2

= y x

xy xy Ri E ,

2

2

1

ba,yfor,0 ≠= y

xy f

−=− y x

xy y xaba jww jww,

)(2

1)(

(**)



HLT/NAACL 2006 67

=

" #

, $ $46 47 & , $ $,L !

= y x

xy xyeff xy Ri Ri,

22

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HLT/NAACL 2006 68

6>

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777

STROU><7

Transition matrixGraph G (V,E )



HLT/NAACL 2006 69

6>

( ' $ $ ' * &

! 4 ; '% (% K% & ) 4 *(Q

, $$ $ $$ &

' 4 ) # '% % # &

HLT/NAACL 2006 70

1,

, 1 , ' A +' 7VSVB ' $ #

–

– $

–

, ' #

– ( 7

– '

– ' $

1*# !' A0 ! 7VVSB# MN



HLT/NAACL 2006 71

1

2

34

5

7

6 8

This graph G has a second graph G’

(not drawn) superimposed on it:

G’ is a uniform transition graph.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8

P a g e R a n k

t=0

00.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8

P a g e R a n k

t=1

(

HLT/NAACL 2006 72

,

0)( =−

=

pG I

pG p

T

T

function PowerStatDist (G):

begin

p(0) = u; (or p(0) = [1,0,…0])

i=1;

repeat

p(i) = E T p(i-1)

L = || p(i)-p(i-1)||1;

i = i + 1;

until L < ε

return p(i)

end

Solution for the

stationary distribution



HLT/NAACL 2006 73

1

2

34

5

7

6 8 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8

P a g e R a n k t=0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8

P a g e R a n k

t=1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8

P a g e R a n k

t=10

0(

HLT/NAACL 2006 74

*1 (



HLT/NAACL 2006 75

'

!' J,

HLT/NAACL 2006 76

;% ' $ ;&

" )$% ; 4 < &

W E + ' )$G



HLT/NAACL 2006 77

>

/ $ &

;% ; *&

% * * &

( L ;% !'&

HLT/NAACL 2006 78



HLT/NAACL 2006 79

HLT/NAACL 2006 80

6 >

!$

– !' &

– ( ' &

– ( M$N & *% M$N&

– , ' $&

– ' $ * ' $ &

– !+ !' $ '

– ,$ J <66<# /"X !' &



HLT/NAACL 2006 81

1

J* * & / $ Y 2$

0 ( .151 &

J, ;&

J$ % && $'' $ & $ &

Honda

Ford

VW

Car and Driver

HLT/NAACL 2006 82

1

1 ' !& ! &

$&

1* ; M3N#

– &&

– 3&&

– &--K , Y

– &&&-Z-3-3$'& – &&-3;-3;&



HLT/NAACL 2006 83

1

J, # – $

;

– * $ + 7666O666

– $

– ' $

1 #

hGa T =

'

Gah ='

)( GGaT

ω = )( T GGh ω = )(G p =

HLT/NAACL 2006 84

1

J, $ ('& ,& &

$ &&% $ &

1* M3N – ! $

– , $ $

– , $ &

1* M$N

– , M N M N

– , MN&



HLT/NAACL 2006 85

1

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, $ $ &

% $ *&

HLT/NAACL 2006 86

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1*#

A

B

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HLT/NAACL 2006 87

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1

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HLT/NAACL 2006 89

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HLT/NAACL 2006 90

'

$

$

-

*



HLT/NAACL 2006 91

* 1 /

, $ $&

– &

– 2 % % $

)

/

–Ex: “chair” – furniture or person

–Ex: “child” – young person or human offspring

“Sit on a chair” “Take a seat on this chair”“The chair of the Math Department” “The chair of the meeting”

HLT/NAACL 2006 92

1 *1/

D - '

0 MN

– $ % $ *

/$ +, (---. +, (---.

/$ +" (-/-.carnivore

wild dogwolf

bearfeline, felidcanine, canidfissiped mamal, fissiped

dachshund

hunting doghyena dogdingo

hyenadog

terrier



HLT/NAACL 2006 93

1 1, 6

# "# 1&& +, " !" (--/.

– 1&& % 4 6&R6 % $ 46&U<

" # – + (--0. +,

(--/.

– $ +1! " 1" (---.

– . $ * +2 " (--0.

−=

D

C C PathC C Similarity

2

),(log),( 21

21

where D is the taxonomy depth

HLT/NAACL 2006 94

( *1/

( $ *

* +3 " *4 (-//. +3" " 3 (-56.

M 2 ! 7 "" !! " & ! " ! ! "N

( * #

7& *& , % &

<& : % % % $ $ % &

>& % C % &



HLT/NAACL 2006 95

(

A very long train traveling along the rails with a constant velocity v in a

certain direction …

train #1: public transport

#2: order set of things

#3: piece of cloth

travel

#1 change location

#2: undergo transportation

rail #1: a barrier

# 2: a bar of steel for trains

#3: a small bird

HLT/NAACL 2006 96

( *1/

* *

– D

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Two U.S. soldiers and an unknown number of civilian

contractors are unaccounted for after a fuel convoy was

attacked near the Baghdad International Airport today,

a senior Pentagon official said. One U.S. soldier and an

Iraqi driver were killed in the incident.…

…

…



HLT/NAACL 2006 99






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HLT/NAACL 2006 100






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HLT/NAACL 2006 104

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HLT/NAACL 2006 105

0

Size(words) Random Lesk TextRank TextRank+Lesk

SemCor

law 825 37.12% 39.62% 46.42% 49.36%

sports 808 29.95% 33.00% 40.59% 46.18%

education 898 37.63% 41.33% 46.88% 52.00%

debates 799 40.17% 42.38% 47.80% 50.52%

en tertainmen t 802 39.27% 43.05% 43.89% 49.31%

Avg. 826 36.82% 39.87% 45.11% 49.47%

Senseval-2

d00 471 28.97% 43.94% 43.94% 47.77%

d01 784 45.47% 52.65% 54.46% 57.39%

d02 514 39.24% 49.61% 54.28% 56.42%

Avg. 590 37.89% 48.73% 50.89% 53.86%

Average (al l) 740 37.22% 43.19% 47.27% 51.16%

“uninformed” (no sense order)

HLT/NAACL 2006 106

0

Size(words) MFS Lesk TextRank TextRank+Lesk

SemCor

law 825 69.09% 72.65% 73.21% 73.97%

sports 808 57.30% 64.21% 68.31% 68.31%

education 898 64.03% 69.33% 71.65% 71.53%

debates 799 66.33% 70.07% 71.14% 71.67%

en tertain men t 802 59.72% 64.98% 6 6.02% 66.16%

Avg. 826 63.24% 68.24% 70.06% 70.32%

Senseval-2

d00 471 51.70% 53.07% 58.17% 57.74%

d01 784 60.80% 64.28% 67.85% 68.11%

d02 514 55.97% 62.84% 63.81% 64.39%

Avg. 590 56.15% 60.06% 63.27% 63.41%Averag e (al l) 740 60.58% 6 5.17% 67.51% 67.72%

“informed” (sense order integrated)



HLT/NAACL 2006 107

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Eyeing the huge market

potential, currently led by

Google, Yahoo took over

search company Overture

Services Inc last year

Yahoo acquired Overture

Applications to:

Information Retrieval (IR)

Comparable Documents (CD)Reading Comprehension (RC)

Question Answering (QA)

Information Extraction (IE)

Machine Translation (MT)Paraphrase Acquisition (PP)



HLT/NAACL 2006 125

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• Dolan, William B., L. Vanderwende, and S. Richardson. 1993. AutomaticallyDeriving Structured Knowledge Base from On-line Dictionaries. In Proceedingsof the Pacific Association for Computational Linguistics, April 21-24, 1993,Vancouver, British Columbia.

• Erkan, G. and Radev, D.. "LexRank: Graph-based Lexical Centrality as Saliencein Text Summarization", Journal of Artificial Intelligence Research , 2004

• Galley, M. and McKeown, K.: Improving Word Sense Disambiguation inLexical Chaining. IJCAI 2003.

• Hirst, G. and St-Onge, D. Lexical chains as representations of context in thedetection and correction of malaproprisms. WordNet: An electronic lexicaldatabase, MIT Press, 1998

• Haghighi, A. and A. Ng and C. Manning, Robust Textual Inference via GraphMatching, EMNLP 2005.

• Halliday, M. and Hasan, R. Cohesion in English. Longman, 1976.• Hobbs, J. A model for natural language semantics, Tech. Report, YaleUniversity, 1974.

• Jannink, J. and G. Wiederhold. Thesaurus entry extraction from an on-linedictionary. Fusion 1999.

• Lee, L. Measures of distributional similarity, ACL 1999.



HLT/NAACL 2006 141

• Lesk, M. Automatic sense disambiguation using machine readabledictionaries: How to tell a pine cone from an ice cream cone. SIGDOC 1986.• Lin, D. An Information-Theoretic Definition of Similarity, ICML 1998.• Mihalcea, R. and Moldovan, D. An iterative approach to word sense

disambiguation. FLAIRS 2000.• Mihalcea, R. and Tarau, P and Figa, E. PageRank on Semantic Networks

with Application to Word Sense Disambiguation, COLING 2004• Mihalcea, R. and Tarau, P. TextRank: Bringing Order Into Texts, EMNLP

2004• Mihalcea, R. Graph-based ranking algorithms for large vocabulary word

sense disambiguation, HTL-EMNLP 2005.• Han, H and Zha, H. and Giles, C.L. Name disambiguation in author citations

using a K-way spectral clustering method, ACM/IEEE – IJCDL 2005• Jannink, J. and Wiederhold, G., Thesaurus Entry Extraction from an On-line

Dictionary, Fusion 1999, Sunnyvale CA, 1999.

• Pang, B. and Lee, L., A Sentimental Education: Sentiment Analysis UsingSubjectivity Summarization Based on Minimum Cuts, ACL 2004.• Widdows, D. and Dorow, B., A Graph Model for Unsupervised Lexical

Acquisition, COLING 2002

HLT/NAACL 2006 142

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HLT/NAACL 2006 145

1

1 (d1s1) Iraqi Vice President Taha Yassin Ramadan announced today, Sunday, that Iraq refuses to back down from its decision to stop

cooperating with disarmament inspectors before its demands are met.

2 (d2s1) Iraqi Vice president Taha Yassin Ramadan announced today, Thursday, that Iraq rejects cooperating with the United Nations

except on the issue of lifting the blockade imposed upon it since the year 1990.

3 (d2s2) Ramadan told reporters in Baghdad that "Iraq cannot deal positively with whoever represents the Security Council unless there was

a clear stance on the issue of lifting the blockade off of it.

4 (d2s3) Baghdad had decided late last October to completely cease cooperating with the inspectors of the United Nations Special

Commission (UNSCOM), in charge of disarming Iraq's weapons, and whose work became very limited since the fifth of August, and

announced it will not resume its cooperation with the Commission even if it were subjected to a military operation.

5 (d3s1) The Russian Foreign Minister, Igor Ivanov, warned today, Wednesday against using force against Iraq, which will destroy,

according to him, seven years of difficult diplomatic work and will complicate the regional situation in the area.

6 (d3s2) Ivanov contended that carrying out air strikes against Iraq, who refuses to cooperate with the United Nations inspectors, ``will end

the tremendous work achieved by the international group during the past seven years and will complicate the situation in the region.''

7 (d3s3) Nevertheless, Ivanov stressed that Baghdad must resume working with the Special Commission in charge of disarming the Iraqi

weapons of mass destruction (UNSCOM).

8 (d4s1) The Special Representative of the United Nations Secretary-General in Baghdad, Prakash Shah, announced today, Wednesday,

after meeting with the Iraqi Deputy Prime Minister Tariq Aziz, that Iraq refuses to back down from its decision to cut off cooperation with

the disarmament inspectors.

9 (d5s1) British Prime Minister Tony Blair said today, Sunday, that the crisis between the international community and Iraq ``did not end''

and that Britain is still ``ready, prepared, and able to strike Iraq.''

10 (d5s2) In a gathering with the press held at the Prime Minister's office, Blair contended that the crisis with Iraq ``will not end until Iraq

has absolutely and unconditionally respected its commitments'' towards the United Nations.

11 (d5s3) A spokesman for Tony Blair had indicated that the British Prime Minister gave permission to British Air Force Tornado planes

stationed in Kuwait to join the aerial bombardment against Iraq.

(DUC cluster d1003t)

HLT/NAACL 2006 146

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HLT/NAACL 2006 147

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HLT/NAACL 2006 149

d4s1

d1s1

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d3s1

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HLT/NAACL 2006 150

d4s1

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d3s2

d3s1

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Sentences vote for the most central sentence!

Need to worry about diversity reranking.

d4s1

d3s2

d2s1



HLT/NAACL 2006 151

N

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T c

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HLT/NAACL 2006 153

HLT/NAACL 2006 154

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HLT/NAACL 2006 155

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HLT/NAACL 2006 156

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HLT/NAACL 2006 157

J &

V x02_join x01_board x0_as x11_director

N x02_is x01_chairman x0_of x11_entitynam

N x02_name x01_director x0_of x11_conglomer

N x02_caus x01_percentag x0_of x11_death

V x02_us x01_crocidolit x0_in x11_filter

V x02_bring x01_attent x0_to x11_problem

Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29. Mr. Vinken is chairman of

Elsevier N.V. , the Dutch publishing group. Rudolph Agnew , 55 years old and former chairman ofConsolidated Gold Fields PLC , was named a nonexecutive director of this British industrial conglomerate.

A form of asbestos once used to make Kent cigarette filters has caused a high percentage of cancer deathsamong a group of workers exposed to it more than 30 years ago , researchers reported . The asbestos fiber ,crocidolite , is unusually resilient once it enters the lungs , with even brief exposures to it causing symptomsthat show up decades later , researchers said . Lorillard Inc. , the unit of New York-based Loews Corp. thatmakes Kent cigarettes , stopped using crocidolite in its Micronite cigarette filters in 1956 . Althoughpreliminary findings were reported more than a year ago , the latest results appear in today 's New EnglandJournal of Medicine , a forum likely to bring new attention to the problem .

HLT/NAACL 2006 158

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HLT/NAACL 2006 160

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HLT/NAACL 2006 161

268 (**)

HLT/NAACL 2006 162

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HLT/NAACL 2006 163

HLT/NAACL 2006 164



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HLT/NAACL 2006 171

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HLT/NAACL 2006 172

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HLT/NAACL 2006 174

0(

Compatibility of systems of linear constraints over the set of natural numbers

Criteria of compatibility of a system of linear Diophantine equations, strictinequations, and nonstrict inequations are considered. Upper bounds for

components of a minimal set of solutions and algorithms of construction of

minimal generating sets of solutions for all types of systems are given.

These criteria and the corresponding algorithms for constructing a minimal

supporting set of solutions can be used in solving all the considered types of

systems and systems of mixed types.

Keywords by TextRank: linear constraints, linear diophantine equations,natural numbers, non-strict inequations, strict inequations, upper bounds

Keywords by human annotators: linear constraints, linear diophantine

equations, non-strict inequations, set of natural numbers, strict inequations,

upper bounds

types

equationssolutions

sets

bounds

numbersnatural

criteria

compatibility

minimal

algorithms

construction components

upper

constraints diophantine

systemlinear

strict

inequations

non-strict

systems

TextRank

numbers (1.46)

inequations (1.45)

linear (1.29)

diophantine (1.28)

upper (0.99)

bounds (0.99)

strict (0.77)

Frequency

systems (4)

types (4)solutions (3)

minimal (3)

linear (2)

inequations (2)

algorit hms (2)



HLT/NAACL 2006 175

0

• Evaluation:

– 500 INSPEC abstracts

– collection previously used in keyphrase extraction[Hulth 2003]

• Various settings. Here:

– nouns and adjectives

– select top N/3

• Evaluation in previous work

– mostly supervised learning – training/development/test : 1000/500/500 abstracts

HLT/NAACL 2006 176

Assigned CorrectMethod Total Mean Total Mean Precision Recall F-measure

Ngram with tag 7,815 15.6 1,973 3.9 25.2 51.7 33.9NP-chunks with tag 4,788 9.6 1,421 2.8 29.7 37.2 33Pattern with tag 7,012 14.0 1,523 3.1 21.7 39.9 28.1TextRank 6,784 13.7 2,116 4.2 31.2 43.1 36.2

(Hulth, 2003)



HLT/NAACL 2006 177

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