W03-1102 |
each paragraph to calculate the
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local clustering
|
score . After obtaining both
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W03-1102 |
each paragraph to calculate the
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local clustering
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score . Our approach is reminiscent
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W03-1102 |
Lmax where Lmax is the maximum
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local clustering
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score using for normalization
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W03-1102 |
score , and L0 is the normalized
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local clustering
|
score . The normalized global
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W10-4168 |
solution . Neill ( 2002 ) used
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local clustering
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, and determined the senses of
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W03-1102 |
properties . We can consider the
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local clustering
|
score as the local property of
|
D14-1031 |
) kn ( kn − 1 ) / 2 The
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local clustering
|
coefficient C ranges between
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W03-1102 |
an algorithm that combines the
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local clustering
|
score with the global connectivity
|
E14-3011 |
network , in terms of average
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local clustering
|
coefficient ( ¯ C ) and
|
W03-1102 |
different views and concepts . The
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local clustering
|
score only captures the content
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W10-0607 |
as fol - lows : 4 We adopt the
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local clustering
|
coefficient of Watts and Strogatz
|
N04-1003 |
we use the same algorithm : A
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local clustering
|
is performed to group mentions
|
W03-1102 |
paragraph score . Therefore , the
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local clustering
|
score for paragraph si can be
|
W13-1732 |
's word net - work , or as the
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local clustering
|
coefficient vector of these words
|
D14-1031 |
neighbors of a node n is called the
|
local clustering
|
coefficient ( C ) ( Watts and
|
P05-3020 |
that for word sense induction the
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local clustering
|
of local vectors is more appropriate
|
P06-2050 |
between characters , and strong
|
local clustering
|
. Moreover , due to its dynamic
|
E14-3011 |
Crand and Lrand are the average
|
local clustering
|
coefficient and the average shortest
|
E14-3011 |
where C and L are the average
|
local clustering
|
coefficient and the average shortest
|
W13-1732 |
neighborhood size ( order 1 ) 4 .
|
local clustering
|
coefficient We take a set of
|