Home > Research on Semantic-based Passive Transformation in Chinese-English Machine Translation Wenfei Chang, Zhiying Liu, Yaohong Jin Institute o
Research on Semantic-based
Passive Transformation in Chinese-English Machine Translation
Wenfei Chang, Zhiying Liu, Yaohong Jin
Institute of Chinese Information Processing
Beijing Normal University
Introduction
1
Semantic analysis of passive voice
2
Transformation rules and algorithm
3
Experiments and Result Analysis
4
Conclusions
5
Outline
1. Introduction
Type
Sentence
number
Proportion
Sentences with
passive mark
390
39%
Sentences without
passive mark
610
61%
2.
Semantic analysis of passive voice
We have investigated 1000 sentences which should be transformed into
English when translating.
Table 1. Classification of Passive Sentence
2.
Semantic analysis of passive voice
Passive
mark BEI
Passive mark SUO
ALL_PASS
passive
voice will be used in English
Verb+ Prep
2.
Semantic analysis of passive voice
1、 “V+NP”
2、 “NP+V”
Component
ellipsis in sentence.
“Verb+Prep”
structure in sentence.
Effect Sentence
2. Semantic analysis of passive voice
A series of rules are drawn up according to several situations .
The specific steps are as fellows:
3. Transformation rules and algorithm
Type
Total number
Should be transformed
Transformed
Right transformed
RB
1000
632
540
481
Google
1000
632
515
430
System
Precision
Recall
RB
89.1%
76.1%
Google
83.4%
68.1%
Table 2. Types of data
Table 3.
Result of transformation
4. Experiments and Result Analysis
4.
Experiments and Result Analysis
By analyzing errors in the result, we find there are mainly have three reasons:
5. Conclusions
Thank you !
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