Concordance

Query P06-2012 14 (652.7 per million)

tech,4-1-P06-2012,bq	This paper presents an <term> unsupervised learning approach </term> to disambiguate various relations between <term> name entities </term> by use of various <term> lexical and syntactic features </term> from the <term> contexts </term> .	#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,12-1-P06-2012,bq	This paper presents an <term> unsupervised learning approach </term> to disambiguate various relations between <term> name entities </term> by use of various <term> lexical and syntactic features </term> from the <term> contexts </term> .	#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,18-1-P06-2012,bq	This paper presents an <term> unsupervised learning approach </term> to disambiguate various relations between <term> name entities </term> by use of various <term> lexical and syntactic features </term> from the <term> contexts </term> .	#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,24-1-P06-2012,bq	This paper presents an <term> unsupervised learning approach </term> to disambiguate various relations between <term> name entities </term> by use of various <term> lexical and syntactic features </term> from the <term> contexts </term> .	#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,4-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </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	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </term> .	#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,10-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </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,14-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </term> .	#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,19-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </term> .	#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,25-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high 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,30-2-P06-2012,bq	It works by calculating <term> eigenvectors </term> of an <term> adjacency graph </term> 's <term> Laplacian </term> to recover a <term> submanifold </term> of data from a <term> high dimensionality space </term> and then performing <term> cluster number estimation </term> on the <term> eigenvectors </term> .	#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,3-3-P06-2012,bq	Experiment results on <term> ACE corpora </term> show that this <term> spectral clustering based approach </term> outperforms the other <term> clustering methods </term> .	#11377 Experiment results onACE corpora show that this spectral clustering based approach outperforms the other clustering methods.
tech,8-3-P06-2012,bq	Experiment results on <term> ACE corpora </term> show that this <term> spectral clustering based approach </term> outperforms the other <term> clustering methods </term> .	#11382 Experiment results on ACE corpora show that thisspectral clustering based approach outperforms the other clustering methods.
tech,15-3-P06-2012,bq	Experiment results on <term> ACE corpora </term> show that this <term> spectral clustering based approach </term> outperforms the other <term> clustering methods </term> .	#11389 Experiment results on ACE corpora show that this spectral clustering based approach outperforms the otherclustering methods.


	in Help