tech,41P062012,bq 
author
</term>
. This paper presents an
<term>

unsupervised learning approach

</term>
to disambiguate various relations

#11320
This paper presents anunsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. 
other,121P062012,bq 
disambiguate various relations between
<term>

name entities

</term>
by use of various
<term>
lexical and

#11328
This paper presents an unsupervised learning approach to disambiguate various relations betweenname entities by use of various lexical and syntactic features from the contexts. 
other,181P062012,bq 
name entities
</term>
by use of various
<term>

lexical and syntactic features

</term>
from the
<term>
contexts
</term>
. It

#11334
This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of variouslexical and syntactic features from the contexts. 
other,241P062012,bq 
syntactic features
</term>
from the
<term>

contexts

</term>
. It works by calculating
<term>
eigenvectors

#11340
This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from thecontexts. 
other,42P062012,bq 
contexts
</term>
. It works by calculating
<term>

eigenvectors

</term>
of an
<term>
adjacency graph
</term>

#11346
It works by calculatingeigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. 
other,72P062012,bq 
calculating
<term>
eigenvectors
</term>
of an
<term>

adjacency graph

</term>
's
<term>
Laplacian
</term>
to recover

#11349
It works by calculating eigenvectors of anadjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. 
other,102P062012,bq 
of an
<term>
adjacency graph
</term>
's
<term>

Laplacian

</term>
to recover a
<term>
submanifold
</term>

#11352
It works by calculating eigenvectors of an adjacency graph'sLaplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. 
other,142P062012,bq 
<term>
Laplacian
</term>
to recover a
<term>

submanifold

</term>
of data from a
<term>
high dimensionality

#11356
It works by calculating eigenvectors of an adjacency graph's Laplacian to recover asubmanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. 
other,192P062012,bq 
<term>
submanifold
</term>
of data from a
<term>

high dimensionality space

</term>
and then performing
<term>
cluster

#11361
It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from ahigh dimensionality space and then performing cluster number estimation on the eigenvectors. 
tech,252P062012,bq 
dimensionality space
</term>
and then performing
<term>

cluster number estimation

</term>
on the
<term>
eigenvectors
</term>
.

#11367
It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performingcluster number estimation on the eigenvectors. 
other,302P062012,bq 
cluster number estimation
</term>
on the
<term>

eigenvectors

</term>
. Experiment results on
<term>
ACE

#11372
It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on theeigenvectors. 
lr,33P062012,bq 
eigenvectors
</term>
. Experiment results on
<term>

ACE corpora

</term>
show that this
<term>
spectral clustering

#11377
Experiment results onACE corpora show that this spectral clustering based approach outperforms the other clustering methods. 
tech,83P062012,bq 
<term>
ACE corpora
</term>
show that this
<term>

spectral clustering based approach

</term>
outperforms the other
<term>
clustering

#11382
Experiment results on ACE corpora show that thisspectral clustering based approach outperforms the other clustering methods. 
tech,153P062012,bq 
approach
</term>
outperforms the other
<term>

clustering methods

</term>
. This paper proposes a novel method

#11389
Experiment results on ACE corpora show that this spectral clustering based approach outperforms the otherclustering methods. 