Concordance

Query P06-2012 14 >
Multilevel Sort 14 (652.7 per million)

lr,3-3-P06-2012,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.
other,10-2-P06-2012,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,12-1-P06-2012,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,14-2-P06-2012,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,18-1-P06-2012,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,19-2-P06-2012,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.
other,24-1-P06-2012,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,30-2-P06-2012,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.
other,4-2-P06-2012,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,7-2-P06-2012,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.
tech,15-3-P06-2012,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.
tech,25-2-P06-2012,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.
tech,4-1-P06-2012,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.
tech,8-3-P06-2012,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.


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