Japanese English Automatic Translation Approach by Susumu Kuno

2. 1. Automatic input editing
2. Automatic segmentation
3. Syntactical analysis
4. Transformation with output editing

3.  Japanese Characteristics
› No spaces
› Kanas and Kanjis
 Thus, requires
› Automatically cutting into components
 However, to prevent too much sized dictionary
› Regulations can be set
 Kana texts in which no kanjis are used
 Kana-kanji texts in which kanjis are used wherever
possible according to the official directives about the
use of kana and kanjis.
› This is “pre-editing”

4.  Each kana will be Romanized
› To preserve
 one-to-one correspondence between kanas and
their correspondent Roman letters
› Better analyzed with Roman letters than kanas
 Fewer varieties of suffixes
 Fewer rules of permissible combinations with
canonical stems
 Fewer possibilities of homographic verbal stems
 Kanji will be replaced with irreducible unit
token
› No kanji will contain more than one
“morpheme”

5.  Segmentation of a continuous run of
tokens
› Based on following prospects:
 Auxiliary items will be shorter in length and
fewer in number
 No problem will be caused by:
 assuming every “phrase” in a sentence begins with a
dictionary item
 including “prefixes” in the category of dictionary items

7.  Predictive analysis:
› Originally by Rhodes
 Peculiarity seen in Japanese :
› More convenient to start from end of sentence:
 Words having a final position in a sentence are
limited
 Particles which show case, prepositional or
conjunctional relationships always follow words,
phrases or clauses to which they are attached
 Attributive words, phrases and clauses always
stand before DT substantives which they modify

8.  Each word in a sentence will be assigned
› An essence which has been fulfilled by it
› A linkage number which shows by which word it
has been predicted
› A group number which shows to which clause in
the sentence it belongs
 Another peculiarity about Japanese:
› The subject of a sentence is very often omitted
 Hence, in this analysis:
› Subject market and relative subject marker
predictions is essential

9.  例)ネズミがネコを殺した話は私を驚かせた.

11.  This stage deals with the synthesis of the TL
 Brief explanation:
› Words with same group num. are gathered
› Transformation of word order is performed
 In concrete:
› Subject marker, object marker & relative subject
marker are omitted
› Subject master or relative subject master comes
first within each group
› followed by predicate head or relative
predicate head
› and then by object master

13.  Readings in Machine Translation
› Edited by Sergei Nirenburg, Harold Somers,
and Yorick Wilks
› The MIT Press