Japanese ( Kawata and Bartels , 2000 ) , despite a very high accuracy , is different in that attachment score drops from 98 % to 85 % , as we go from length 1 to 2 , which may have something to do with the data consisting of transcribed speech with very short utterances .
stylebook for the japanese treebank in verbmobil verbmobilreport 240 seminar f¨ur sprachwissenschaft
Y Kawata J Bartels
Typical examples are Bulgarian ( Simov et al. , 2005 ; Simov and Osenova , 2003 ) , Chinese ( Chen et al. , 2003 ) , Danish ( Kromann , 2003 ) , and Swedish ( Nilsson et al. , 2005 ) . Japanese ( Kawata and Bartels , 2000 ) , despite a very high accuracy , is different in that attachment score drops from 98 % to 85 % , as we go from length 1 to 2 , which may have something to do with the data consisting of transcribed speech with very short utterances . A second observation is that a high proportion of non-projective structures leads to fragmentation in the parser output , reflected in lower precision for roots .
Japanese ( @@CITATION ) , despite a very high accuracy , is different in that attachment score drops from 98 % to 85 % , as we go from length 1 to 2 , which may have something to do with the data consisting of transcribed speech with very short utterances .