P02-1052 to explore ways to improve the similarity scoring algorithm . For instance , we
J10-3003 in the cluster according to a similarity scoring function . For this approach
P02-1052 cause a to become a . An improved similarity scoring algorithm may in turn result
D14-1116 Mikolov et al. , 2010 ) . The similarity scoring methods are introduced in Section
S13-1027 more dominant in affecting the similarity scoring than others . We performed a
D09-1122 templates have slots that are used for similarity scoring . The second one was obtained
P14-2124 training pipeline . The phrasal similarity scoring has only been minimally adapted
S15-2008 paraphrase detection and pairwise similarity scoring tasks . To classify a pair of
J14-3006 algorithm and the computation of the similarity scoring function . A standard agglomerative
K15-1019 an ensemble with the semantic similarity scoring functions , the results improve
D14-1170 papers collected , we apply a similarity scoring method to assign the importance
S15-2008 downstream paraphrase detection and similarity scoring . We would also like to explore
P02-1052 using the following formula : This similarity scoring provides the basis for our newly
S13-1007 ) regression problem , where a similarity scoring function between text pairs is
P02-1052 N66001-00-1-9814 . <title> Using Similarity Scoring To Improve the Bilingual Dictionary
S15-2008 autoencoders for paraphrase detection and similarity scoring as a part of SemEval 2015 Task
J03-3002 to compare tsim with structural similarity scoring , we applied it to 325 English-French
S12-1088 ) regression problem , where a similarity scoring function between text pairs is
C02-1002 based on two heuristics : string similarity scoring and relative distance . The similarity
W00-0405 similarity metric and use the passage similarity scoring as a method of clustering passages
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