W03-0314 into bilingual sequences to which sequential pattern mining is applied . By doing so , we
W03-0314 correspondences from parallel corpora by sequential pattern mining . The main characteristics of
D15-1258 these features ( patterns ) using sequential pattern mining . We prepare a small dataset
W03-0314 well . In this paper , we apply sequential pattern mining to solve the problem . First
D15-1258 Background : State of the art sequential pattern mining algorithms require the dataset
P07-2017 . Different from conventional sequential pattern mining method , in feature combination
W03-0314 length . These characteristics in sequential pattern mining leads us to the idea of concatenating
P07-2017 problem , we employ the well-known sequential pattern mining algo - rithm , namely PrefixSpan
P07-1011 words/tags . Existing frequent sequential pattern mining algorithms ( e.g. ( Pei et al.
D15-1258 extracted suggestion templates using sequential pattern mining and hashtags . ( Wicaksono and
W03-0314 In what follows , we describe sequential pattern mining and each module in Figure 1 .
N10-1108 patterns can be extracted by the sequential pattern mining algorithm PrefixSpan ( Pei et
P13-1157 capture characteristics in N-grams . Sequential Pattern Mining It is costly to manually collect
P13-1157 min support E N threshold , the sequential pattern mining finds all frequent subsequences
P04-1016 . After that , we can employ a sequential pattern mining technique to select statistically
P03-1004 is an efficient algorithm for sequential pattern mining , originally proposed by ( Pei
P13-1157 Phrase Selection As a result of sequential pattern mining , we can gather a huge number
W03-0314 2.1 Sequential Pattern Mining Sequential pattern mining discovers frequent subsequences
W03-0314 each module in Figure 1 . 2.1 Sequential Pattern Mining Sequential pattern mining discovers
W03-0314 Correspondences from Parallel </title> via Sequential Pattern Mining Abstract We present an unsupervised
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