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