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Machine translation method and system

A technology of machine translation and translation results, applied in the field of natural language processing research, which can solve the problems of large number of redundant rules and poor performance of machine translation

Active Publication Date: 2017-04-26
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0015] In order to solve the problem that the number of redundant rules of the extracted hierarchical phrase translation rules in the prior art is very large, resulting in very poor machine translation performance, the embodiment of the present invention provides a machine translation method and system

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Embodiment Construction

[0108] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0109] figure 1 It is a method flow chart of the machine translation method provided in an embodiment of the present invention. The machine translation method can be applied to a computer. The computer can be a machine or device for language translation, or it can be used for performing language translation before language translation. A machine or device for the training process. The machine translation method may include a translation step, which may include:

[0110] Step 101: Obtain an input source language test sentence;

[0111] Step 102: Perform lexical analysis on the source language test sentence to obtain the lexical analysis result of the source language test sentence;

[0112] Step 103: Perform syntactic structure analys...

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Abstract

The invention discloses a machine translation method and system and belongs to the field of natural language processing research. The machine translation method comprises obtaining a source language testing sentence; respectively obtaining a lexical analysis result and a syntactic analysis result; extracting a PAS (Predicate Argument Structure) according to the syntactic analysis result; performing structural transferring on the PAS according to syntactic features of a target language; translating the source language testing sentence according to a transferred PAS structure and a translation rule obtained after training. According to the machine translation method, PAS transferring based statistic machine translation is achieved through transferring processing of the PAS according to sentence structure information and syntactic information of the PAS and the syntactic analysis result at a source language end, the problem that the number of redundancy rules translation rules in the prior art is large and accordingly the machine translation performance is poor, and effects of improving the global order adjusting performance of a sentence structure, reducing the number of extracted translation rules and improving the translation quality are effectively achieved.

Description

technical field [0001] The invention relates to the field of natural language processing research, in particular to a machine translation method and system. Background technique [0002] At present, the mainstream methods of machine translation can be roughly divided into rule-based and corpus-based methods, and corpus-based methods can be subdivided into instance-based methods and statistics-based methods. The machine translation method based on statistics can use the bilingual parallel corpus as the learning data, automatically obtain the translation rules through training, and at the same time, combine the language model to translate the test sentences. With the continuous improvement of statistical machine translation models, word-based, phrase-based and syntactic structure-based statistical models have appeared successively, and the translation quality has been continuously improved. [0003] However, traditional statistical machine translation models make more use of ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/28G06F17/27
Inventor 徐金安刘江鸣吴培昊
Owner BEIJING JIAOTONG UNIV
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