eigenvectors
</term>
. Experiment results on
<term>
ACE corpora
</term>
show that this
<term>
spectral clustering
#12314Experiment results onACE corpora show that this spectral clustering based approach outperforms the other clustering methods.
other,7-2-P06-2012,ak
calculating
<term>
eigenvectors
</term>
of an
<term>
adjacency graph 's Laplacian
</term>
to recover a
<term>
submanifold
</term>
#12286It 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.
tech,25-2-P06-2012,ak
dimensionality space
</term>
and then performing
<term>
cluster number estimation
</term>
on the
<term>
eigenvectors
</term>
.
#12304It 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.
tech,15-3-P06-2012,ak
approach
</term>
outperforms the other
<term>
clustering methods
</term>
. This paper proposes a novel method
#12326Experiment results on ACE corpora show that this spectral clustering based approach outperforms the otherclustering methods.
other,24-1-P06-2012,ak
syntactic features
</term>
from the
<term>
contexts
</term>
. It works by calculating
<term>
eigenvectors
#12277This paper presents an unsupervised learning approach to disambiguate various relations between named entities by use of various lexical and syntactic features from thecontexts.
other,4-2-P06-2012,ak
contexts
</term>
. It works by calculating
<term>
eigenvectors
</term>
of an
<term>
adjacency graph 's Laplacian
#12283It 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,30-2-P06-2012,ak
cluster number estimation
</term>
on the
<term>
eigenvectors
</term>
. Experiment results on
<term>
ACE
#12309It 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.
other,19-2-P06-2012,ak
<term>
submanifold
</term>
of data from a
<term>
high dimensionality space
</term>
and then performing
<term>
cluster
#12298It 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.
other,18-1-P06-2012,ak
named entities
</term>
by use of various
<term>
lexical and syntactic features
</term>
from the
<term>
contexts
</term>
. It
#12271This paper presents an unsupervised learning approach to disambiguate various relations between named entities by use of variouslexical and syntactic features from the contexts.
other,12-1-P06-2012,ak
various
<term>
relations
</term>
between
<term>
named entities
</term>
by use of various
<term>
lexical and
#12265This paper presents an unsupervised learning approach to disambiguate various relations betweennamed entities by use of various lexical and syntactic features from the contexts.
other,10-1-P06-2012,ak
approach
</term>
to disambiguate various
<term>
relations
</term>
between
<term>
named entities
</term>
#12263This paper presents an unsupervised learning approach to disambiguate variousrelations between named entities by use of various lexical and syntactic features from the contexts.
tech,8-3-P06-2012,ak
<term>
ACE corpora
</term>
show that this
<term>
spectral clustering based approach
</term>
outperforms the other
<term>
clustering
#12319Experiment results on ACE corpora show that thisspectral clustering based approach outperforms the other clustering methods.
other,14-2-P06-2012,ak
graph 's Laplacian
</term>
to recover a
<term>
submanifold
</term>
of data from a
<term>
high dimensionality
#12293It 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.
tech,4-1-P06-2012,ak
unique author . This paper presents an
<term>
unsupervised learning approach
</term>
to disambiguate various
<term>
relations
#12257This paper presents anunsupervised learning approach to disambiguate various relations between named entities by use of various lexical and syntactic features from the contexts.