P98-1070 well-balanced translation units based on a semantic distance calculation . The splitting is performed
A94-1017 Here , we briefly introduce the semantic distance calculation on an AM ( Associative Memory
C96-1070 best-only substructures using semantic distance calculations . In this paper , we will first
A00-1006 structural disambiguation using semantic distance calculations , in parallel with the derivation
P98-2141 disambiguation of patterns by semantic distance calculation . Our scheme determines the best
P98-2141 retain only one substructure in the semantic distance calculation in order to confirm the feasibility
W97-0109 our purposes , we have based the semantic distance calculation on a combination of the path
P98-1070 wellbalanced translation units based on a semantic distance calculation . The complete translation result
P98-1070 structural disambiguation using semantic distance calculations , in parallel with the derivation
A94-1017 increases . TDMT utilizes the semantic distance calculation proposed by Sumita and Iida (
W97-0404 from speech recognition results semantic distance calculation , and its application to speech
W09-3303 exploited for some WordNet-based semantic distances calculation taking into account the depth
W98-0701 any of the participants in the semantic distance calculation is a function ( derived from
C96-1070 retains only one substructure using semantic distance calculation . The translation times are measured
P98-2141 with head word-information using semantic distance calculations when combined incrementally with
C96-1070 infbrination , which is used for semantic distance calculation when combining with other structures
A94-1017 T801 Transputer ( 25 MHz ) . 3.2 Semantic Distance Calculation on APs As described in subsection
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