P06-1115 parsers . SVM classifiers based on string subsequence kernels are trained for each of the productions
D12-1039 Compared with the other methods , the string subsequence kernel delivers encouraging results
C04-1064 DTPs based on the ESK ( Extended String Subsequence Kernel ) , which considers sequential
C04-1064 , we introduce the " Extended String Subsequence Kernel " ( ESK ) for semantic information
D08-1032 . They also introduce Extended String Subsequence Kernel ( ESK ) to incorporate semantics
D08-1032 syntactic information . They apply String Subsequence Kernel ( SSK ) to measure the similarity
C04-1064 to its extension , the Extended String Subsequence Kernel ( ESK ) . First , we describe
S14-2006 machine learning method with a string subsequence kernel . As well as training data consisting
P09-2083 similarity mea - sure , and Extended String Subsequence Kernel ( ESSK ) . The representative
J15-1001 texts and applying the Extended String Subsequence Kernel to calculate their similarity
J15-1001 color , apple . 3.3.2 Extended String Subsequence Kernel ( ESSK ) . Once we identify the
P06-1115 production pi using a normalized string subsequence kernel . Following the framework of
W03-1208 Collins and Duffy , 2001 ) and String Subsequence Kernel ( SSK ) ( Lodhi et al. , 2002
P03-1005 Collins and Duffy , 2001 ) and String Subsequence Kernel ( SSK ) ( Lodhi et al. , 2002
D12-1039 Contractor et al. , 2010 ) ) , and a string subsequence kernel ( Lodhi et al. , 2002 ) . In
P09-2092 uses a modified version of the String Subsequence Kernel of Shawe-Taylor and Christianini
J15-1001 of texts and apply the Extended String Subsequence Kernel ( ESSK ) ( Hirao et al. 2003
D08-1042 programming algorithm to compute string subsequence kernels in O ( nst ) time where s and
C04-1064 translations ( Papineni et al. , 2002 ) . String Subsequence Kernel ( SSK ) ( Lodhi et al. , 2002
W03-0402 method was used in the proof of the string subsequence kernel ( Cristianini and Shawe-Tayor
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