C92-2097 provided by the wide application of distance calculation . TDMT achieves efficient translation
C92-2097 example-based framework is the distance calculation . This framework achieves the
A94-1017 briefly introduce the semantic distance calculation on an AM ( Associative Memory
C90-2049 Section 3 . An explanation of distance calculation is given in Section 4 . Another
A94-1038 words and the example words . The distance calculation method of ( Sumita , 91 ) is
C90-2049 mechanisms of path search and distance calculation . A precise description of path
C90-2049 preferable by doing path search and distance calculation . After resolving one ambiguous
A00-1006 disambiguation using semantic distance calculations , in parallel with the derivation
C94-1007 details of the distance and total distance calculations . ) For instance , when the pattern
C94-1007 structures are formed based on the distance calculation . The numbers in brackets represent
C92-2097 idea of TDMT . Section 3 explains distance calculation and transfer in an examplebased
C92-2097 3.1 Word distance We adopt the distance calculation method of Example - Based Machine
C92-2097 Example-based Transfer TDMT utilizes distance calculation to determine the most plausible
C94-1007 mechanism of this framework it ; the distance calculation \ -LSB- 4 \ -RSB- , \ -LSB- 5
C94-1015 lilizcd for extracting an input for distance calculations ( See section 4.3 ) . X noun-verb
A94-1017 increases . TDMT utilizes the semantic distance calculation proposed by Sumita and Iida (
C02-1011 . The key components are ( 1 ) distance calculation by KL divergence ( 2 ) EM , (
C92-2097 determines the English expression by distance calculation with examples before and after
C94-1007 -LSB- 5 \ -RSB- , which conducts distance calculations between linguistic expressions
C94-1015 more dclailed . 4.3 lnl ) ut of ' distance calculation An input for distance ealcuh.ltion
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