CN107357783A - A kind of English translation mass analysis method of translator of Chinese into English - Google Patents

A kind of English translation mass analysis method of translator of Chinese into English Download PDF

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CN107357783A
CN107357783A CN201710535667.5A CN201710535667A CN107357783A CN 107357783 A CN107357783 A CN 107357783A CN 201710535667 A CN201710535667 A CN 201710535667A CN 107357783 A CN107357783 A CN 107357783A
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translation
english translation
english
word
standard
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CN107357783B (en
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黄桂敏
吴闯
黄思睿
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Guilin University of Electronic Technology
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Guilin University of Electronic Technology
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking

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Abstract

The present invention provides a kind of English translation mass analysis method of translator of Chinese into English, this method be one by be linked in sequence translation English translation pretreatment module, translation English translation informativeness analysis module, translation English translation semantic similarity analysis module, translate the analysis model that English translation quality analysis results generation module forms.After one translation English translation is handled by the analysis model, the quality analysis results of this translation English translation can be finally obtained.The method of the present invention solves translator of Chinese and automatically analyzes problem into the English translation quality of English, and its analysis result is more preferable into the analysis result of the English translation mass analysis method of English than traditional translator of Chinese.

Description

A kind of English translation mass analysis method of translator of Chinese into English
Technical field
The present invention relates to statistics natural language processing technique, translator of Chinese into English translation in word syntactic analysis skill Art, translator of Chinese into the content analysis techniques in English, be specifically a kind of translator of Chinese into English English translation quality analysis Method.
Background technology
Traditional translator of Chinese mainly has complete matching process, editing distance into the English translation mass analysis method of English Method, based on N metagrammar methods.The English translation requirement that complete matching process translates into English to Chinese is excessively tight, only serves as interpreter When the content of English translation is identical with the content of standard English translation, just judge that the English translation of translation is correct, this side Method have ignored a word multiform problem of English word completely.Edit distance approach translates into the English translation quality of English to Chinese Analysis method is：Check that translation English translation transforms to insertion, deletion, the replacement time of English word during standard English translation Number, the translation quality of translation English translation is then analyzed according to these numbers, but this method can not check translation English Whether the word between language translation and standard English translation is synonym.N metagrammar methods are to occurring in translation English translation N tuples matched with the N tuples occurred in standard English translation, English translation is analyzed according to the number that the match is successful Translation quality, but this method can not check the semantic similarity between translation English translation and standard English translation.This Invention is in order to solve the above problems, there is provided a kind of English translation mass analysis method of translator of Chinese into English.
The content of the invention
The present invention translator of Chinese into the English translation mass analysis method of English overall process flow as shown in figure 1, Including translation English translation pretreatment module, translation English translation informativeness analysis module, translation English translation semanteme phase Like degree analysis module, translation English translation quality analysis results generation module.
It is therein translation English translation pretreatment module handling process be：First, English translation and standard are translated in input English translation, translation English translation and standard English translation are segmented respectively, word small letter, removal stop words；Second, To participle, word small letter, the translation English translation for removing stop words and standard English translation carry out part-of-speech tagging, part of speech disappears Discrimination, phrase stripping and slicing；3rd, export the translation English translation of above-mentioned two-step pretreatment and the pre-processed results of standard English translation.
It is therein translation English translation informativeness analysis module handling process be：First, input translation English translation, mark Quasi- English translation, translation English translation pre-processed results, standard English translation pre-processed results, numbering translation English translation, mark Word in quasi- English translation, translation English translation pre-processed results, standard English translation pre-processed results；Second, to translation Word in English translation with the word in standard English translation accurately match and the successful word of record matching and its volume Number；3rd, remaining word is accurately matched to translation English translation and standard English translation and carries out stem matching and record matching Successful word and its numbering；4th, remaining word is matched to translation English translation and standard English translation stem and carried out together Adopted word matching and the successful word of record matching and its numbering；5th, translated using training translation English translation collection and standard English The total words that the match is successful in the word matched rate accuracy rate weight and translation English translation of collected works generation, which are calculated and exported, to be turned over Translate the informativeness of English translation.
It is therein translation English translation semantic similarity analysis module handling process be：First, input translation English is translated Text, standard English translation part of speech disambiguation result；Second, searched according to part of speech disambiguation result and record word in English Semantic dictionary In corresponding node serial number；3rd, in English Semantic dictionary, found from node serial number corresponding to translation English translation word Node serial number corresponding to standard English word, record on accessed path has corresponding to each word between node serial number and node To the probability on side；4th, according to corresponding to each word on accessed path between node serial number and node directed edge probability, it is raw The word probability distribution bivector into translation English translation, and iterate to calculate out word probability distribution two in standard English translation Dimensional vector；5th, merge word probability distribution bivector in translation English translation and obtain translating English translation probability distribution two Dimensional vector, and word probability value is extracted from translation English translation probability distribution bivector, generation translation English translation probability It is distributed one-dimensional vector；6th, merge standard English translation word probability distribution bivector and obtain standard English translation probability point Cloth bivector, and word probability value is extracted from standard English translation probability distribution bivector, generate standard English translation Probability distribution one-dimensional vector；7th, according to translation English translation probability distribution one-dimensional vector and standard English translation probability distribution One-dimensional vector calculates and exports translation English translation semantic similarity.
It is therein translation English translation quality analysis results generation module handling process be：First, input translation English The output result of translation informativeness analysis module, the output result for translating English translation semantic similarity analysis module；Second, root According to the output knot of the output result of translation English translation informativeness analysis module, translation English translation semantic similarity analysis module The mass fraction and comment of fruit generation translation English translation.
The present invention is defined as follows：
1st, word part-of-speech tagging collection
The present invention is using University of Pennsylvania of U.S. Binzhou treebank mark collection, according to the rule of the mark collection to translating English translation Word part-of-speech tagging is carried out with standard English translation.
2nd, word part-of-speech tagging structure
Word part-of-speech tagging is to carry out part-of-speech tagging processing to the word in translation English translation and standard English translation, under Face is the form after part-of-speech tagging：
Word1[part of speech1# parts of speech2# parts of speech3...] word2[part of speech1# parts of speech2# parts of speech3……]……
Wordn[part of speech1# parts of speech2# parts of speech3……]
3rd, phrase stripping and slicing structure
Phrase stripping and slicing is to carry out cutting to the noun phrase in translation English translation and standard English translation and verb phrase, Here is the form of phrase stripping and slicing：
Word1The stripping and slicing of/phrase1, word2The stripping and slicing of/phrase2... ... wordnThe stripping and slicing of/phrasen
4th, part of speech disambiguation structure
Part of speech disambiguation refers to, will translate the word part-of-speech tagging result and English glossary of English translation and standard English translation Web-Based Dictionary compares, and therefrom removes the word part-of-speech tagging result of marking error, and here is the form after part of speech disambiguation：
Word1[part of speech1# parts of speech2# parts of speech3...] word2[part of speech1# parts of speech2# parts of speech3……]……
Wordn[part of speech1# parts of speech2# parts of speech3……]
5th, English Semantic dictionary
English Semantic dictionary refers to alphabet sequence tissue entry information, and based on the semantic word of the multiple lexical or textual analysis of word Allusion quotation, the dictionary contain common noun, verb, adjective, adverbial word and the major part of function word five, and the form of English Semantic dictionary is such as Under：
Word1[part of speech1] [word frequency1] [offset1] [lexicon file detailed path1] [meaning of a word lexical or textual analysis1] [meaning of a word corresponding node Numbering1] [directed edge probability between node1]
Word1[part of speech2] [word frequency2] [offset2] [lexicon file detailed path2] [meaning of a word lexical or textual analysis2] [meaning of a word corresponding node Numbering2] [directed edge probability between node2]
……
Word1[part of speechn] [word frequencyn] [offsetn] [lexicon file detailed pathn] [meaning of a word lexical or textual analysisn] [meaning of a word corresponding node Numberingn] [directed edge probability between noden]
Word2[part of speech1] [word frequency1] [offset1] [lexicon file detailed path1] [meaning of a word lexical or textual analysis1] [meaning of a word corresponding node Numbering1] [directed edge probability between node1]
Word2[part of speech2] [word frequency2] [offset2] [lexicon file detailed path2] [meaning of a word lexical or textual analysis2] [meaning of a word corresponding node Numbering2] [directed edge probability between node2]
……
Word2[part of speechn] [word frequencyn] [offsetn] [lexicon file detailed pathn] [meaning of a word lexical or textual analysisn] [meaning of a word corresponding node Numberingn] [directed edge probability between noden]
Wordn[part of speech1] [word frequency1] [offset1] [lexicon file detailed path1] [meaning of a word lexical or textual analysis1] [meaning of a word corresponding node Numbering1] [directed edge probability between node1]
Wordn[part of speech2] [word frequency2] [offset2] [lexicon file detailed path2] [meaning of a word lexical or textual analysis2] [meaning of a word corresponding node Numbering2] [directed edge probability between node2]
……
Wordn[part of speechn] [word frequencyn] [offsetn] [lexicon file detailed pathn] [meaning of a word lexical or textual analysisn] [meaning of a word corresponding node Numberingn] [directed edge probability between noden]
6th, word matched accuracy rate calculation formula
Word matched accuracy rate refers to translate word matched success number between English translation and standard English translation and turned over The ratio of total words in English translation is translated, its calculation formula is as follows：
7th, word matched recall rate calculation formula
Word matched recall rate refers to translate word matched success number and mark between English translation and standard English translation The ratio of total words in quasi- English translation, its calculation formula are as follows：
8th, harmonic-mean calculation formula
Harmonic-mean refers to that the word matched of the word matched accuracy rate and calculation formula (2) of calculation formula (1) is recalled Average value between rate, its calculation formula are as follows：
In calculation formula (3), word matched success rate is calculated by calculation formula (1), word matched recall rate by Calculation formula (2) is calculated, and a is the weight of word matched accuracy rate, and 1-a is the weight of word matched recall rate.
9th, penalty coefficient value calculation formula
Penalty coefficient value be reduce translation English translation content and standard English translation content caused by harmonic-mean it Between error coefficient, its calculation formula is as follows：
In calculation formula (4), phrase number refers to translate the sum of noun phrase and verb phrase in English translation；B is The adjusting parameter of penalty coefficient value size；C is phrase number with translating the word number ratio size that the match is successful in English translation Adjusting parameter；B and c is calculated by translation English translation informativeness analysis module.
10th, English translation informativeness calculation formula is translated
Translation English translation informativeness refers to that the word for translating English translation is similar to the semanteme of word of standard English translation Degree, its calculation formula are as follows：
Translate English translation informativeness=(1- penalty coefficients value) × harmonic-mean (5)
In calculation formula (5), harmonic average numerical value is calculated by calculation formula (3), and penalty coefficient value is public by calculating Formula (4) is calculated.
11st, ProbabilityDistribution Vector calculation formula
ProbabilityDistribution Vector refers in English Semantic dictionary, from node checks standard corresponding to translation English translation word Node corresponding to English word, the probability of directed edge is formed between node serial number and node corresponding to each word on accessed path Vector, its calculation formula is as follows：
ProbabilityDistribution Vectort=(1-a) × adjacency matrix × ProbabilityDistribution Vectort-1+ a × ProbabilityDistribution Vector0 (6)
In calculation formula (6), t represents the number searched, and a represents the probability of directed edge on the t times accessed path, adjacent Matrix represents the word adjacency matrix in English Semantic dictionary, ProbabilityDistribution Vector0Represent the probability of lookup original position node Distribution vector, ProbabilityDistribution Vectort-1Represent the ProbabilityDistribution Vector of the t-1 times lookup node, ProbabilityDistribution VectortRepresent t The secondary ProbabilityDistribution Vector for searching node.
12nd, English translation Semantic Similarity Measurement formula is translated
Translation English translation semantic similarity refers to translate semantic between English translation content and standard English translation content Similarity degree, its calculation formula are as follows：
In calculation formula (7), translation English translation probability distribution one-dimensional vector refers in English Semantic dictionary, from turning over Translate corresponding to English translation word node serial number and search node serial number corresponding to standard English word, each word on accessed path The one-dimensional vector that the probability of directed edge is formed between corresponding node；Standard English translation probability distribution one-dimensional vector refers in English In language semantic dictionary, node serial number corresponding to standard English word is searched from node serial number corresponding to translation English translation word, The one-dimensional vector that the probability of the directed edge of accessed path end position node connection is formed；| | translation English translation probability distribution one Dimensional vector | | refer to the mould for translating English translation probability distribution one-dimensional vector；| | standard English translation probability distribution one-dimensional vector | | Refer to the mould of standard English translation probability distribution one-dimensional vector.
13rd, English translation mass fraction calculation formula is translated
Translation English translation mass fraction refers to the content and the contents semantic phase of standard English translation for translating English translation It is as follows like degree, its calculation formula：
English translation mass fraction=0.5 × English translation+0.5 × English translation of informativeness semantic similarity (8)
In calculation formula (8), translation English translation informativeness score is calculated by calculation formula (5), translates English Translation semantic similarity is calculated by calculation formula (7).
Specific steps
The translation English translation pretreatment module of analysis method of the present invention, translate English translation informativeness analysis module, turn over It is as follows to translate English translation semantic similarity analysis module, the process chart of translation English translation quality analysis results generation module It is described.
As shown in Fig. 2 described translation English translation pretreatment module handling process is as follows：
P201 starts；
P202 reads in translation English translation and standard English translation；
P203 judges to translate English translation and whether standard English translation quantity is identical, if it is turns P204, otherwise turns P202；
P204 is to translation English translation participle and will translate the word small letter after English translation segments；
The word segmentation result of P205 output translation English translations and the word small letter result of translation English translation；
P206 statistical translation English translation total words；
P207 carries out part-of-speech tagging to the word segmentation result for translating English translation；
P208 carries out phrase stripping and slicing to the part-of-speech tagging result for translating English translation；
P209 carries out part of speech disambiguation to the phrase stripping and slicing result for translating English translation；
The part-of-speech tagging result of P210 output translation English translations, the part of speech disambiguation result for translating English translation, translation English The phrase stripping and slicing result of language translation；
P211 to standard English translation segment and by standard English translation segment after word small letter；
The word segmentation result of P212 outputting standard English translations and the word small letter result of standard English translation；
P213 SSs English batch translation total words；
P214 carries out part-of-speech tagging to the word segmentation result of standard English translation；
P215 carries out phrase stripping and slicing to the part-of-speech tagging result of standard English translation；
Phrase stripping and slicing results of the P216 to standard English translation carries out part of speech disambiguation；
The part-of-speech tagging result of P217 outputting standard English translations, the part of speech disambiguation result of standard English translation, standard English The phrase stripping and slicing result of language translation；
P218 terminates.
As shown in figure 3, described translation English translation informativeness analysis module handling process is as follows：
P301 starts；
P302 reads in the word segmentation result of translation English translation and the word small letter result of translation English translation；
P303 reads in the word segmentation result of standard English translation and the word small letter result of standard English translation；
P304 is according to the word segmentation result of translation English translation and word small letter result, the standard English of translation English translation The word segmentation result of translation and the word small letter result of standard English translation, generation translation English translation-standard English translation Text pair；
P305 reads in the text pair of one group of translation English translation-standard English translation；
P306 starts from scratch the text pair translation each word of English translation for translating English translation-standard English translation Numbering；
P307 starts from scratch each word of text pair standard English translation for translating English translation-standard English translation Numbering；
The text pair for translating English translation-standard English translation is translated each word of English translation by P308, and is turned over The each word of standard English translation for translating the text pair of English translation-standard English translation is accurately matched；
The text pair translation English translation of P309 statistical translations English translation-standard English translation, and translation English The accurate word that the match is successful of the text pair standard English translation of translation-standard English translation and numbering；
It is inaccurate that P310 judges whether the text pair translation English translation for translating English translation-standard English translation also has The word of matching, if it is turn P308, otherwise turn P311；
The text pair for translating English translation-standard English translation is translated English translation by P311, and translation English is translated The text pair standard English translation of text-standard English translation carries out stemmed without the accurate word that the match is successful；
Word in the text pair translation English translation of translation English translation-standard English translation after P312 will be stemmed, And it is stemmed after translation English translation-standard English translation text pair standard English translation word carry out stem Match somebody with somebody；
The text pair translation English translation of P313 statistical translations English translation-standard English translation, and translation English The text pair standard English translation stem word that the match is successful and numbering of translation-standard English translation；
P314 judges that the text pair for translating English translation-standard English translation translates in English translation whether also have non-word The word of desiccation matching, if it is turns P312, otherwise turns P315；
The text pair for translating English translation-standard English translation is translated English translation by P315, and translation English is translated The text pair standard English translation of text-standard English translation does not have the stem word that the match is successful to carry out meaning of a word lexical or textual analysis；
P316 translates the text pair that English translation-standard English translation is translated after the meaning of a word lexical or textual analysis list of English translation The word that the text pair standard English translation of English translation-standard English translation is translated after word, and meaning of a word lexical or textual analysis is carried out Synonym matches；
The text pair translation English translation of P317 statistical translations English translation-standard English translation, and translation English The text pair standard English translation synonym word that the match is successful and numbering of translation-standard English translation；
P318 judges whether the text pair for translating English translation-standard English translation is translated also not same in English translation The word of adopted word matching, if it is turns P315, otherwise turns P319；
P319 judges whether the text pair for the translation English translation-standard English translation do not corrected also, if it is turns P305, otherwise turn P320；
P320 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation The result that the text pair standard English translation word of English translation-standard English translation accurately matches；
P321 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation The result of the text pair standard English translation word stem matching of English translation-standard English translation；
P322 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation The result of the text pair standard English translation word synonym matching of English translation-standard English translation；
P323 marks the text pair translation English translation list that the match is successful of translation English translation-standard English translation Word；
P324 reads in the text pair translation English translation total words of translation English translation-standard English translation；
P325 reads in the text pair standard English translation total words of translation English translation-standard English translation；
P326 translates the text pair for translating English translation-standard English translation the total words of English translation, and Total words in the text pair standard English translation of English translation-standard English translation are translated, translate English translation-standard English The text pair translation English translation of language translation is accurately matched, stem matches, the synonym total words that the match is successful, and substitution is turned over Translate the calculation formula (1) of English translation informativeness analysis module, calculation formula (2) calculates translation English translation-standard English and translated The accuracy rate and recall rate of the text pair translation English translation word matched of text；
P327 is using translating English translation collection and the training of standard English collection of translations draws optimized parameter a, b, c value；
The text pair of a value, translation English translation-standard English translation is translated English translation word matched by P328 Accuracy rate, the recall rate for the text pair translation English translation word matched for translating English translation-standard English translation are substituted into and turned over The calculation formula (3) for translating English translation informativeness analysis module calculates harmonic-mean；
The value of parameter b, c, the text pair of translation English translation-standard English translation are translated English translation phrase by P329 Quantity, translation English translation-standard English translation text pair translation English translation accurately match, stem matching, it is synonymous The word total words that the match is successful, the calculation formula (4) for substituting into translation English translation informativeness analysis module calculate penalty coefficient；
Harmonic-mean, penalty coefficient are substituted into the calculation formula (5) of translation English translation informativeness analysis module by P330 Calculate translation English translation informativeness；
P331 terminates.
As shown in figure 4, described translation English translation semantic similarity analysis module handling process is as follows：
P401 starts；
P402 reads translation English translation word part of speech disambiguation result；
P403 ergodic translation English translation word part of speech disambiguation results；
P404 according to translation English translation word part of speech disambiguation result search and record word in English Semantic dictionary it is right The node serial number answered；
P405 judges whether the translation English translation word part of speech disambiguation result not traveled through also, if it is turns P403, Otherwise P406 is turned；
P406 reads standard English translation word part of speech disambiguation result；
P407 travels through standard English translation word part of speech disambiguation result；
P408 searches according to standard English translation word part of speech disambiguation result and to record word right in English Semantic dictionary The node serial number answered；
P409 judges whether the standard English translation word part of speech disambiguation result not traveled through also, if it is turns P407, Otherwise P410 is turned；
P410 searches standard English word in English Semantic dictionary, from node serial number corresponding to translation English translation word Corresponding node serial number；
P411 records on accessed path the probability of directed edge between node serial number and node corresponding to each word；
The probability of P412 directed edges between node serial number and node according to corresponding to each word on accessed path, generation are turned over Translate English translation word probability distribution bivector；
P413 calculates standard English translation according to the calculation formula (6) of translation English translation semantic similarity analysis module Middle word probability is distributed bivector；
P414 judges to translate in English translation whether also have the word do not searched, if it is turns P410, otherwise turns P415；
P415 merges translation English translation word probability distribution bivector and obtains translating English translation probability distribution two dimension Vector；
P416 extracts word probability value from translation English translation probability distribution bivector, and generation translation English translation is general Rate is distributed one-dimensional vector；
P417 merges standard English translation word probability distribution bivector and obtains standard English translation probability distribution two dimension Vector；
P418 extracts word probability value from standard English translation probability distribution bivector, and generation standard English translation is general Rate is distributed one-dimensional vector；
P419 is calculated according to the calculation formula (7) of translation English translation semantic similarity analysis module and is exported translation English Translation semantic similarity；
P420 terminates.
As shown in figure 5, described translation English translation quality analysis results generation module handling process is as follows：
P501 starts；
P502 reads translation English translation informativeness；
P503 reads translation English translation semantic similarity；
P504 is calculated according to the calculation formula (8) of translation English translation quality analysis results generation module and is exported translation English Language translation quality fraction；
P505 is according to translation English translation mass fraction output translation English translation comment；
P506 terminates.
Brief description of the drawings
Fig. 1 is the overall process flow figure of the inventive method；
Fig. 2 is the translation English translation pretreatment module process chart of the inventive method；
Fig. 3 is the translation English translation informativeness analysis module process chart of the inventive method；
Fig. 4 is the translation English translation semantic similarity analysis module process chart of the inventive method；
Fig. 5 is the translation English translation quality analysis results generation module process chart of the inventive method.
Embodiment
The present invention it is a kind of in translate English prose translation quality analysis method embodiment be divided into it is following five step Suddenly.
First step：Perform " translation English translation pretreatment module "
The English translation inputted in embodiment of the present invention has drawn from six grades of national college students' English translation topics, and Chinese turns over Translate topic, standard English translation result that certain student answers the English translation of translation, official provide it is as follows：
Translator of Chinese topic：
The innovation of China just flourishes at an unprecedented rate.In order to catch up with and surpass world's prosperity as early as possible in science and technology Country, China have increased considerably research and development fund in recent years.The university and research institute of China are actively developing innovation and ground Study carefully, these researchs are covered from big data to biochemistry, all kinds of high-tech areas such as from new energy to robot.They also with The Technology Park cooperation of various regions, is commercialized innovation achievement.At the same time, no matter on product or business model, Chinese Enterprise Family also is making great efforts to strive the pioneer for doing and innovating, to adapt to the demand that domestic and international consumption market constantly changes and increased.
Translate English translation：
Innovation is progressing in an unprecedented speed in China.In order to catch up with those developed countries in the world as fast as it can in the science and technology field,China has increased funds for development research substantially in recent years.Universities and research institutions in China are actively carrying out innovation researches,which cover high- technology fields such as big data,biochemistry,new energy and robots, etc.They also cooperate with science park in various regions,commercializing the research results of innovation.Meanwhile,no matter in production and business model,entrepreneurs in China are competing to be pioneers in innovation to adapt to the constantly changing and increasing needs of the consumer market at home and abroad.
Standard English translation：
China's innovation is flourishing faster than ever before.In order to surpass developed countries on science and technology as soon as possible, China has sharply increased research and development fund.Chinese universities and institutes are actively doing innovative researches,covering various fields of high technology,from big data to biochemistry,and from new energy to robots.They are also cooperating with science and technology parks in different places,so as to commercialize their fruits of innovation.In the meantime,to adapt to the changing foreign and domestic market,and to satisfy the growing demand,Chinese entrepreneurs are also making pioneering efforts to innovate their products and business models.
(1) after carrying out part-of-speech tagging to translation English translation and standard English translation, the part-of-speech tagging result of generation is as follows It is shown：
Translate English translation part-of-speech tagging result：
[Innovation[innovation#NN:UN*],[#null*],is[be#VBZ*],[#null*], progressing[progress#VBG*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*],an[a# DT*],[#null*],unprecedented[unprecedented#JJ*],[#null*],speed[speed#NN:UN*, speed#VB*,speed#VBP*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*],China[China# NNP*,china#JJ*,china#NN:U*],.[.#.*]]
[In[In#NNP*,in#IN*,in#NN*,in#RP*],[#null*],order[order#NN:UN*,order# UH*,order#VB*,order#VBP*],[#null*],to[to#IN*,to#TO*],[#null*],catch[catch# NN*,catch#VB*,catch#VBP*],[#null*],up[up#IN*,up#JJ*,up#NN*,up#RP*,up#VB*,up# VBP*],[#null*],with[with#IN*,with#RP*],[#null*],those[those#DT*],[#null*], developed[develop#VBD*,develop#VBN*],[#null*],countries[country#NNS*],[# null*],in[in#IN*,in#NN*,in#RP*],[#null*],the[the#DT*],[#null*],world[world# JJ*,world#NN:UN*],[#null*],as[as#CC*,as#IN*,as#RB*],[#null*],fast[fast#JJ*, fast#NN*,fast#RP*,fast#VB*,fast#VBP*],[#null*],as[as#CC*,as#IN*,as#RB*],[# null*],it[it#PRP*],[#null*],can[can#MD*,can#NN*,can#VB*,can#VBP*],[#null*],in [in#IN*,in#NN*,in#RP*],[#null*],the[the#DT*],[#null*],science[science#NN: UN*],[#null*],and[and#CC*],[#null*],technology[technology#NN:UN*],[#null*], field[field#NN*,field#VB*,field#VBP*],,[,#,*],[#null*],China[China#NNP*, china#JJ*,china#NN:U*],[#null*],has[have#VBZ*],[#null*],increased[increase# VBD*,increase#VBN*],[#null*],funds[fund#NNS*,fund#VBZ*],[#null*],for[for#CC*, for#IN*,for#RP*],[#null*],development[development#NN:UN*],[#null*],research [research#NN:U*,research#VB*,research#VBP*],[#null*],substantially [substantially#RB*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*],recent[recent# JJ*],[#null*],years[year#NNS*],.[.#.*]]
[Universities[university#NNS*],[#null*],and[and#CC*],[#null*], research[research#NN:U*,research#VB*,research#VBP*],[#null*],institutions [institution#NNS*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*],China[China# NNP*,china#JJ*,china#NN:U*],[#null*],are[are#NN*,be#VBP*],[#null*],actively [actively#RB*],[#null*],carrying[carry#VBG*],[#null*],out[out#IN*,out#NN*, out#RB*,out#RP*,out#UH*,out#VB*,out#VBP*],[#null*],innovation[innovation#NN: UN*],[#null*],researches[research#NNS*,research#VBZ*],,[,#,*],[#null*],which [which#WDT*,which#WP*],[#null*],cover[cover#NN:UN*,cover#VB*,cover#VBP*],[# null*],high-technology[high-technology#JJ*],[#null*],fields[field#NNS*,field# VBZ*],[#null*],such[such#DT*,such#PDT*],[#null*],as[as#CC*,as#IN*,as#RB*],[# null*],big[big#JJ*,big#RB*],[#null*],data[data#NN:UN*,datum#NNS*],,[,#,*],[# null*],biochemistry[biochemistry#NN:U*],,[,#,*],[#null*],new[new#JJ*],[# null*],energy[energy#NN:UN*],[#null*],and[and#CC*],[#null*],robots[robot# NNS*],,[,#,*],[#null*],etc[etc#null*],.[.#.*],[#null*],They[they#PRP*],[# null*],also[also#RB*],[#null*],cooperate[cooperate#VB*,cooperate#VBP*],[# null*],with[with#IN*,with#RP*],[#null*],science[science#NN:UN*],[#null*],park [park#NN*,park#VB*,park#VBP*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*], various[various#JJ*],[#null*],regions[region#NNS*],,[,#,*],[#null*], commercializing[commercialize#VBG*],[#null*],the[the#DT*],[#null*],research [research#NN:U*,research#VB*,research#VBP*],[#null*],results[result#NNS*, result#VBZ*],[#null*],of[of#IN*],[#null*],innovation[innovation#NN:UN*],. [.#.*]]
[Meanwhile[meanwhile#JJ*,meanwhile#NN:U*,meanwhile#RB*],,[,#,*],[# null*],no[no#DT*,no#NN*],[#null*],matter[matter#NN:UN*,matter#VB*,matter# VBP*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*],production[production#NN: UN*],[#null*],and[and#CC*],[#null*],business[business#JJ*,business#NN:UN*],[# null*],model[model#JJ*,model#NN*,model#VB*,model#VBP*],,[,#,*],[#null*], entrepreneurs[entrepreneur#NNS*],[#null*],in[in#IN*,in#NN*,in#RP*],[#null*], China[China#NNP*,china#JJ*,china#NN:U*],[#null*],are[are#NN*,be#VBP*],[# null*],competing[compete#VBG*],[#null*],to[to#IN*,to#TO*],[#null*],be[be# VB*],[#null*],pioneers[pioneer#NNS*,pioneer#VBZ*],[#null*],in[in#IN*,in#NN*, in#RP*],[#null*],innovation[innovation#NN:UN*],[#null*],to[to#IN*,to#TO*],[# null*],adapt[adapt#VB*,adapt#VBP*],[#null*],to[to#IN*,to#TO*],[#null*],the [the#DT*],[#null*],constantly[constantly#RB*],[#null*],changing[change#VBG*], [#null*],and[and#CC*],[#null*],increasing[increasing#JJ*,increasing#NN:UN*, increase#VBG*],[#null*],needs[needs#RB*,need#NNS*,need#VBZ*],[#null*],of[of# IN*],[#null*],the[the#DT*],[#null*],consumer[consumer#NN:UN*],[#null*],market [market#NN:UN*,market#VB*,market#VBP*],[#null*],at[at#IN*,at#RP*],[#null*], home[home#JJ*,home#NN:UN*,home#VB*,home#VBP*],[#null*],and[and#CC*],[#null*], abroad[abroad#JJ*,abroad#RB*],.[.#.*]]
Standard English translation part-of-speech tagging result：
[China[China#NNP*,china#JJ*,china#NN:U*],'['#null*],s[s#POS*],[# null*],innovation[innovation#NN:UN*],[#null*],is[be#VBZ*],[#null*], flourishing[flourishing#JJ*,flourish#VBG*],[#null*],faster[faster#NN*,faster# RB*,faster#RBR*,fast#JJR*],[#null*],than[than#IN*],[#null*],ever[ever#JJ*, ever#RB*,ever#RP*],[#null*],before[before#IN*,before#RP*],.[.#.*]]
[In[In#NNP*,in#IN*,in#NN*,in#RP*],[#null*],order[order#NN:UN*,order# UH*,order#VB*,order#VBP*],[#null*],to[to#IN*,to#TO*],[#null*],surpass [surpass#VB*,surpass#VBP*],[#null*],developed[develop#VBD*,develop#VBN*],[# null*],countries[country#NNS*],[#null*],on[on#IN*,on#JJ*,on#RP*],[#null*], science[science#NN:UN*],[#null*],and[and#CC*],[#null*],technology[technology# NN:UN*],[#null*],as[as#CC*,as#IN*,as#RB*],[#null*],soon[soon#JJ*,soon#RB*],[# null*],as[as#CC*,as#IN*,as#RB*],[#null*],possible[possible#JJ*,possible# NN*],,[,#,*],[#null*],China[China#NNP*,china#JJ*,china#NN:U*],[#null*],has [have#VBZ*],[#null*],sharply[sharply#RB*],[#null*],increased[increase#VBD*, increase#VBN*],[#null*],research[research#NN:U*,research#VB*,research#VBP*], [#null*],and[and#CC*],[#null*],development[development#NN:UN*],[#null*],fund [fund#NN:UN*,fund#VB*,fund#VBP*],.[.#.*]]
[Chinese[Chinese#JJ*,Chinese#NN:U*,Chinese#NNP*],[#null*], universities[university#NNS*],[#null*],and[and#CC*],[#null*],institutes [institute#NNS*,institute#VBZ*],[#null*],are[are#NN*,be#VBP*],[#null*], actively[actively#RB*],[#null*],doing[doing#NN:UN*,do#VBG*],[#null*], innovative[innovative#JJ*],[#null*],researches[research#NNS*,research#VBZ*],, [,#,*],[#null*],covering[covering#JJ*,covering#NN:UN*,cover#VBG*],[#null*], various[various#JJ*],[#null*],fields[field#NNS*,field#VBZ*],[#null*],of[of# IN*],[#null*],high[high#JJ*,high#NN*,high#RP*],[#null*],technology [technology#NN:UN*],,[,#,*],[#null*],from[from#IN*,from#RP*],[#null*],big [big#JJ*,big#RB*],[#null*],data[data#NN:UN*,datum#NNS*],[#null*],to[to#IN*, to#TO*],[#null*],biochemistry[biochemistry#NN:U*],,[,#,*],[#null*],and[and# CC*],[#null*],from[from#IN*,from#RP*],[#null*],new[new#JJ*],[#null*],energy [energy#NN:UN*],[#null*],to[to#IN*,to#TO*],[#null*],robots[robot#NNS*],. [.#.*]]
[They[they#PRP*],[#null*],are[are#NN*,be#VBP*],[#null*],also[also# RB*],[#null*],cooperating[cooperate#VBG*],[#null*],with[with#IN*,with#RP*],[# null*],science[science#NN:UN*],[#null*],and[and#CC*],[#null*],technology [technology#NN:UN*],[#null*],parks[park#NNS*,park#VBZ*],[#null*],in[in#IN*, in#NN*,in#RP*],[#null*],different[different#JJ*],[#null*],places[place#NNS*, place#VBZ*],,[,#,*],[#null*],so[so#CC*,so#JJ*,so#NN:U*],[#null*],as[as#CC*, as#IN*,as#RB*],[#null*],to[to#IN*,to#TO*],[#null*],commercialize [commercialize#VB*,commercialize#VBP*],[#null*],their[their#PRP$*],[#null*], fruits[fruit#NNS*,fruit#VBZ*],[#null*],of[of#IN*],[#null*],innovation [innovation#NN:UN*],.[.#.*]]
[In[In#NNP*,in#IN*,in#NN*,in#RP*],[#null*],the[the#DT*],[#null*], meantime[meantime#JJ*,meantime#NN:U*],,[,#,*],[#null*],to[to#IN*,to#TO*],[# null*],adapt[adapt#VB*,adapt#VBP*],[#null*],to[to#IN*,to#TO*],[#null*],the [the#DT*],[#null*],changing[change#VBG*],[#null*],foreign[foreign#JJ*],[# null*],and[and#CC*],[#null*],domestic[domestic#JJ*,domestic#NN*],[#null*], market[market#NN:UN*,market#VB*,market#VBP*],,[,#,*],[#null*],and[and#CC*],[# null*],to[to#IN*,to#TO*],[#null*],satisfy[satisfy#VB*,satisfy#VBP*],[#null*], the[the#DT*],[#null*],growing[growing#NN:UN*,grow#VBG*],[#null*],demand [demand#NN:UN*,demand#VB*,demand#VBP*],,[,#,*],[#null*],Chinese[Chinese#JJ*, Chinese#NN:U*,Chinese#NNP*],[#null*],entrepreneurs[entrepreneur#NNS*],[# null*],are[are#NN*,be#VBP*],[#null*],also[also#RB*],[#null*],making[making# NN:UN*,make#VBG*],[#null*],pioneering[pioneer#VBG*],[#null*],efforts[effort# NNS*],[#null*],to[to#IN*,to#TO*],[#null*],innovate[innovate#VB*,innovate# VBP*],[#null*],their[their#PRP$*],[#null*],products[product#NNS*],[#null*], and[and#CC*],[#null*],business[business#JJ*,business#NN:UN*],[#null*],models [model#NNS*,model#VBZ*],.[.#.*]]
(2) after carrying out phrase stripping and slicing to translation English translation and standard English translation, the phrase stripping and slicing result of generation is as follows It is shown：
Translate English translation phrase stripping and slicing result：
/Innovation/innovation#NN:UN*,B-NP-singular|E-NP-singular/,/#null*/, is/be#VBZ*,B-VP/,/#null*/,progressing/progress#VBG*,I-VP/,/#null*/,in/in#IN*, in#NN*,in#RP*,B-PP/,/#null*/,an/a#DT*,B-NP-singular/,/#null*/,unprecedented/ unprecedented#JJ*,I-NP-singular/,/#null*/,speed/speed#NN:UN*,speed#VB*,speed# VBP*,E-NP-singular/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,China/ China#NNP*,china#JJ*,china#NN:U*,B-NP-singular|E-NP-singular/,./.#.*,O//
/In/In#NNP*,in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,order/order#NN:UN*, order#UH*,order#VB*,order#VBP*,B-NP-singular|E-NP-singular/,/#null*/,to/to# IN*,to#TO*,B-VP/,/#null*/,catch/catch#NN*,catch#VB*,catch#VBP*,I-VP/,/# null*/,up/up#IN*,up#JJ*,up#NN*,up#RP*,up#VB*,up#VBP*,B-PRT/,/#null*/,with/ with#IN*,with#RP*,B-PP/,/#null*/,those/those#DT*,B-NP-plural/,/#null*/, developed/develop#VBD*,develop#VBN*,I-NP-plural/,/#null*/,countries/country# NNS*,E-NP-plural/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,the/the# DT*,B-NP-singular/,/#null*/,world/world#JJ*,world#NN:UN*,E-NP-singular/,/# null*/,as/as#CC*,as#IN*,as#RB*,B-ADVP/,/#null*/,fast/fast#JJ*,fast#NN*,fast# RP*,fast#VB*,fast#VBP*,I-ADVP/,/#null*/,as/as#CC*,as#IN*,as#RB*,B-SBAR/,/# null*/,it/it#PRP*,B-NP-singular|E-NP-singular/,/#null*/,can/can#MD*,can#NN*, can#VB*,can#VBP*,B-VP/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,the/ the#DT*,B-NP-singular/,/#null*/,science/science#NN:UN*,I-NP-singular/,/# null*/,and/and#CC*,I-NP-singular/,/#null*/,technology/technology#NN:UN*,I-NP- singular/,/#null*/,field/field#NN*,field#VB*,field#VBP*,E-NP- singular/,,/,#,*,O/,/#null*/,China/China#NNP*,china#JJ*,china#NN:U*,B-NP- singular|E-NP-singular/,/#null*/,has/have#VBZ*,B-VP/,/#null*/,increased/ increase#VBD*,increase#VBN*,I-VP/,/#null*/,funds/fund#NNS*,fund#VBZ*,B-NP- plural|E-NP-plural/,/#null*/,for/for#CC*,for#IN*,for#RP*,B-PP/,/#null*/, development/development#NN:UN*,B-NP-singular/,/#null*/,research/research#NN: U*,research#VB*,research#VBP*,E-NP-singular/,/#null*/,substantially/ substantially#RB*,B-ADVP/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/, recent/recent#JJ*,B-NP-plural/,/#null*/,years/year#NNS*,E-NP-plural/,./.#.*, O//
/Universities/university#NNS*,B-NP-plural/,/#null*/,and/and#CC*,I-NP- plural/,/#null*/,research/research#NN:U*,research#VB*,research#VBP*,I-NP- plural/,/#null*/,institutions/institution#NNS*,E-NP-plural/,/#null*/,in/in# IN*,in#NN*,in#RP*,B-PP/,/#null*/,China/China#NNP*,china#JJ*,china#NN:U*,B-NP- singular|E-NP-singular/,/#null*/,are/are#NN*,be#VBP*,B-VP/,/#null*/,actively/ actively#RB*,I-VP/,/#null*/,carrying/carry#VBG*,I-VP/,/#null*/,out/out#IN*, out#NN*,out#RB*,out#RP*,out#UH*,out#VB*,out#VBP*,B-PRT/,/#null*/,innovation/ innovation#NN:UN*,B-NP-plural/,/#null*/,researches/research#NNS*,research# VBZ*,E-NP-plural/,,/,#,*,O/,/#null*/,which/which#WDT*,which#WP*,B-NP-singular |E-NP-singular/,/#null*/,cover/cover#NN:UN*,cover#VB*,cover#VBP*,B-VP/,/# null*/,high-technology/high-technology#JJ*,B-NP-plural/,/#null*/,fields/ field#NNS*,field#VBZ*,E-NP-plural/,/#null*/,such/such#DT*,such#PDT*,B-PP/,/# null*/,as/as#CC*,as#IN*,as#RB*,I-PP/,/#null*/,big/big#JJ*,big#RB*,B-NP- plural/,/#null*/,data/data#NN:UN*,datum#NNS*,E-NP-plural/,,/,#,*,O/,/#null*/, biochemistry/biochemistry#NN:U*,B-NP-singular|E-NP-singular/,,/,#,*,O/,/# null*/,new/new#JJ*,B-NP-plural/,/#null*/,energy/energy#NN:UN*,I-NP-plural/,/# null*/,and/and#CC*,I-NP-plural/,/#null*/,robots/robot#NNS*,E-NP- plural/,,/,#,*,O/,/#null*/,etc/etc#null*,B-NP-singular|E-NP-singular/,./.#.*, O/,/#null*/,They/they#PRP*,B-NP-singular|E-NP-singular/,/#null*/,also/also# RB*,B-ADVP/,/#null*/,cooperate/cooperate#VB*,cooperate#VBP*,B-VP/,/#null*/, with/with#IN*,with#RP*,B-PP/,/#null*/,science/science#NN:UN*,B-NP- singular/,/#null*/,park/park#NN*,park#VB*,park#VBP*,E-NP-singular/,/#null*/, in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,various/various#JJ*,B-NP-plural/,/# null*/,regions/region#NNS*,E-NP-plural/,,/,#,*,O/,/#null*/,commercializing/ commercialize#VBG*,B-VP/,/#null*/,the/the#DT*,B-NP-plural/,/#null*/,research/ research#NN:U*,research#VB*,research#VBP*,I-NP-plural/,/#null*/,results/ result#NNS*,result#VBZ*,E-NP-plural/,/#null*/,of/of#IN*,B-PP/,/#null*/, innovation/innovation#NN:UN*,B-NP-singular|E-NP-singular/,./.#.*,O//
/Meanwhile/meanwhile#JJ*,meanwhile#NN:U*,meanwhile#RB*,B- ADVP/,,/,#,*,O/,/#null*/,no/no#DT*,no#NN*,B-NP-singular/,/#null*/,matter/ matter#NN:UN*,matter#VB*,matter#VBP*,E-NP-singular/,/#null*/,in/in#IN*,in# NN*,in#RP*,B-PP/,/#null*/,production/production#NN:UN*,B-NP-singular/,/# null*/,and/and#CC*,I-NP-singular/,/#null*/,business/business#JJ*,business#NN: UN*,I-NP-singular/,/#null*/,model/model#JJ*,model#NN*,model#VB*,model#VBP*,E- NP-singular/,,/,#,*,O/,/#null*/,entrepreneurs/entrepreneur#NNS*,B-NP-plural| E-NP-plural/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,China/China# NNP*,china#JJ*,china#NN:U*,B-NP-singular|E-NP-singular/,/#null*/,are/are#NN*, be#VBP*,B-VP/,/#null*/,competing/compete#VBG*,I-VP/,/#null*/,to/to#IN*,to# TO*,I-VP/,/#null*/,be/be#VB*,I-VP/,/#null*/,pioneers/pioneer#NNS*,pioneer# VBZ*,B-NP-plural|E-NP-plural/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/# null*/,innovation/innovation#NN:UN*,B-NP-singular|E-NP-singular/,/#null*/,to/ to#IN*,to#TO*,B-VP/,/#null*/,adapt/adapt#VB*,adapt#VBP*,I-VP/,/#null*/,to/to# IN*,to#TO*,B-PP/,/#null*/,the/the#DT*,B-NP-singular/,/#null*/,constantly/ constantly#RB*,I-NP-singular/,/#null*/,changing/change#VBG*,E-NP-singular/,/# null*/,and/and#CC*,O/,/#null*/,increasing/increasing#JJ*,increasing#NN:UN*, increase#VBG*,B-VP/,/#null*/,needs/needs#RB*,need#NNS*,need#VBZ*,B-NP-plural| E-NP-plural/,/#null*/,of/of#IN*,B-PP/,/#null*/,the/the#DT*,B-NP-singular/,/# null*/,consumer/consumer#NN:UN*,I-NP-singular/,/#null*/,market/market#NN:UN*, market#VB*,market#VBP*,E-NP-singular/,/#null*/,at/at#IN*,at#RP*,B-PP/,/# null*/,home/home#JJ*,home#NN:UN*,home#VB*,home#VBP*,B-NP-singular|E-NP- singular/,/#null*/,and/and#CC*,O/,/#null*/,abroad/abroad#JJ*,abroad#RB*,B- ADVP/,./.#.*,O//
Standard English translation phrase stripping and slicing result：
/China/China#NNP*,china#JJ*,china#NN:U*,B-NP-singular|E-NP- singular/,'/'#null*/,s/s#POS*/,/#null*/,innovation/innovation#NN:UN*,E-NP- singular/,/#null*/,is/be#VBZ*,B-VP/,/#null*/,flourishing/flourishing#JJ*, flourish#VBG*,I-VP/,/#null*/,faster/faster#NN*,faster#RB*,faster#RBR*,fast# JJR*,B-ADVP/,/#null*/,than/than#IN*,B-PP/,/#null*/,ever/ever#JJ*,ever#RB*, ever#RP*,B-ADVP/,/#null*/,before/before#IN*,before#RP*,I-ADVP/,./.#.*,O//
/In/In#NNP*,in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,order/order#NN:UN*, order#UH*,order#VB*,order#VBP*,B-NP-singular|E-NP-singular/,/#null*/,to/to# IN*,to#TO*,B-VP/,/#null*/,surpass/surpass#VB*,surpass#VBP*,I-VP/,/#null*/, developed/develop#VBD*,develop#VBN*,B-NP-plural/,/#null*/,countries/country# NNS*,E-NP-plural/,/#null*/,on/on#IN*,on#JJ*,on#RP*,B-PP/,/#null*/,science/ science#NN:UN*,B-NP-singular/,/#null*/,and/and#CC*,I-NP-singular/,/#null*/, technology/technology#NN:UN*,E-NP-singular/,/#null*/,as/as#CC*,as#IN*,as#RB*, B-ADVP/,/#null*/,soon/soon#JJ*,soon#RB*,I-ADVP/,/#null*/,as/as#CC*,as#IN*,as# RB*,B-PP/,/#null*/,possible/possible#JJ*,possible#NN*,B-ADJP/,,/,#,*,O/,/# null*/,China/China#NNP*,china#JJ*,china#NN:U*,B-NP-singular|E-NP-singular/,/# null*/,has/have#VBZ*,B-VP/,/#null*/,sharply/sharply#RB*,I-VP/,/#null*/, increased/increase#VBD*,increase#VBN*,I-VP/,/#null*/,research/research#NN:U*, research#VB*,research#VBP*,B-NP-singular/,/#null*/,and/and#CC*,I-NP- singular/,/#null*/,development/development#NN:UN*,I-NP-singular/,/#null*/, fund/fund#NN:UN*,fund#VB*,fund#VBP*,E-NP-singular/,./.#.*,O//
/Chinese/Chinese#JJ*,Chinese#NN:U*,Chinese#NNP*,B-NP-plural/,/# null*/,universities/university#NNS*,I-NP-plural/,/#null*/,and/and#CC*,I-NP- plural/,/#null*/,institutes/institute#NNS*,institute#VBZ*,E-NP-plural/,/# null*/,are/are#NN*,be#VBP*,B-VP/,/#null*/,actively/actively#RB*,I-VP/,/# null*/,doing/doing#NN:UN*,do#VBG*,I-VP/,/#null*/,innovative/innovative#JJ*,B- NP-plural/,/#null*/,researches/research#NNS*,research#VBZ*,E-NP- plural/,,/,#,*,O/,/#null*/,covering/covering#JJ*,covering#NN:UN*,cover#VBG*, B-VP/,/#null*/,various/various#JJ*,B-NP-plural/,/#null*/,fields/field#NNS*, field#VBZ*,E-NP-plural/,/#null*/,of/of#IN*,B-PP/,/#null*/,high/high#JJ*,high# NN*,high#RP*,B-NP-singular/,/#null*/,technology/technology#NN:UN*,E-NP- singular/,,/,#,*,O/,/#null*/,from/from#IN*,from#RP*,B-PP/,/#null*/,big/big# JJ*,big#RB*,B-NP-plural/,/#null*/,data/data#NN:UN*,datum#NNS*,E-NP-plural/,/# null*/,to/to#IN*,to#TO*,B-VP/,/#null*/,biochemistry/biochemistry#NN:U*,I- VP/,,/,#,*,O/,/#null*/,and/and#CC*,O/,/#null*/,from/from#IN*,from#RP*,B- PP/,/#null*/,new/new#JJ*,B-NP-singular/,/#null*/,energy/energy#NN:UN*,E-NP- singular/,/#null*/,to/to#IN*,to#TO*,B-PP/,/#null*/,robots/robot#NNS*,B-NP- plural|E-NP-plural/,./.#.*,O//
/They/they#PRP*,B-NP-singular|E-NP-singular/,/#null*/,are/are#NN*,be# VBP*,B-VP/,/#null*/,also/also#RB*,I-VP/,/#null*/,cooperating/cooperate#VBG*, I-VP/,/#null*/,with/with#IN*,with#RP*,B-PP/,/#null*/,science/science#NN:UN*, B-NP-plural/,/#null*/,and/and#CC*,I-NP-plural/,/#null*/,technology/ technology#NN:UN*,I-NP-plural/,/#null*/,parks/park#NNS*,park#VBZ*,E-NP- plural/,/#null*/,in/in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,different/different# JJ*,B-NP-plural/,/#null*/,places/place#NNS*,place#VBZ*,E-NP-plural/,,/,#,*, O/,/#null*/,so/so#CC*,so#JJ*,so#NN:U*,O/,/#null*/,as/as#CC*,as#IN*,as#RB*, O/,/#null*/,to/to#IN*,to#TO*,B-VP/,/#null*/,commercialize/commercialize#VB*, commercialize#VBP*,I-VP/,/#null*/,their/their#PRP$*,B-NP-plural/,/#null*/, fruits/fruit#NNS*,fruit#VBZ*,E-NP-plural/,/#null*/,of/of#IN*,B-PP/,/#null*/, innovation/innovation#NN:UN*,B-NP-singular|E-NP-singular/,./.#.*,O//
/In/In#NNP*,in#IN*,in#NN*,in#RP*,B-PP/,/#null*/,the/the#DT*,B-NP- singular/,/#null*/,meantime/meantime#JJ*,meantime#NN:U*,E-NP- singular/,,/,#,*,O/,/#null*/,to/to#IN*,to#TO*,B-VP/,/#null*/,adapt/adapt#VB*, adapt#VBP*,I-VP/,/#null*/,to/to#IN*,to#TO*,B-PP/,/#null*/,the/the#DT*,B-NP- singular/,/#null*/,changing/change#VBG*,I-NP-singular/,/#null*/,foreign/ foreign#JJ*,I-NP-singular/,/#null*/,and/and#CC*,I-NP-singular/,/#null*/, domestic/domestic#JJ*,domestic#NN*,I-NP-singular/,/#null*/,market/market#NN: UN*,market#VB*,market#VBP*,E-NP-singular/,,/,#,*,O/,/#null*/,and/and#CC*, O/,/#null*/,to/to#IN*,to#TO*,B-VP/,/#null*/,satisfy/satisfy#VB*,satisfy#VBP*, I-VP/,/#null*/,the/the#DT*,B-NP-singular/,/#null*/,growing/growing#NN:UN*, grow#VBG*,I-NP-singular/,/#null*/,demand/demand#NN:UN*,demand#VB*,demand# VBP*,E-NP-singular/,,/,#,*,O/,/#null*/,Chinese/Chinese#JJ*,Chinese#NN:U*, Chinese#NNP*,B-NP-plural/,/#null*/,entrepreneurs/entrepreneur#NNS*,E-NP- plural/,/#null*/,are/are#NN*,be#VBP*,B-VP/,/#null*/,also/also#RB*,I-VP/,/# null*/,making/making#NN:UN*,make#VBG*,I-VP/,/#null*/,pioneering/pioneer#VBG*, B-NP-plural/,/#null*/,efforts/effort#NNS*,E-NP-plural/,/#null*/,to/to#IN*,to# TO*,B-VP/,/#null*/,innovate/innovate#VB*,innovate#VBP*,I-VP/,/#null*/,their/ their#PRP$*,B-NP-plural/,/#null*/,products/product#NNS*,I-NP-plural/,/# null*/,and/and#CC*,I-NP-plural/,/#null*/,business/business#JJ*,business#NN: UN*,I-NP-plural/,/#null*/,models/model#NNS*,model#VBZ*,E-NP-plural/,./.#.*, O//
(3) after carrying out part of speech disambiguation to translation English translation and standard English translation, the part of speech disambiguation result of generation is as follows It is shown：
Translate English translation part of speech disambiguation result：
<S>Innovation[innovation#NN:UN,B-NP-singular|E-NP-singular]is[be#VBZ, B-VP]progressing[progress#VBG,I-VP]in[in#IN,B-PP]an[a#DT,B-NP-singular] unprecedented[unprecedented#JJ,I-NP-singular]speed[speed#NN:UN,E-NP-singular] in[in#IN,B-PP]China[China#NNP,B-NP-singular|E-NP-singular].[.#.,<#S>,O]
<S>In[in#IN,B-PP]order[order#NN:UN,B-NP-singular|E-NP-singular]to[to# IN,to#TO,B-VP]catch[catch#NN,catch#VB,catch#VBP,I-VP]up[up#IN,up#JJ,up#NN,up# RP,up#VB,up#VBP,B-PRT]with[with#IN,with#RP,B-PP]those[those#DT,B-NP-plural] developed[develop#VBD,develop#VBN,I-NP-plural]countries[country#NNS,E-NP- plural]in[in#IN,B-PP]the[the#DT,B-NP-singular]world[world#NN:UN,E-NP- singular]as[as#RB,B-ADVP]fast[fast#JJ,fast#NN,fast#RP,fast#VB,fast#VBP,I- ADVP]as[as#RB,B-SBAR]it[it#PRP,B-NP-singular|E-NP-singular]can[can#MD,B-VP]in [in#IN,B-PP]the[the#DT,B-NP-singular]science[science#NN:UN,I-NP-singular]and [and#CC,I-NP-singular]technology[technology#NN:UN,I-NP-singular]field[field# NN,field#VB,field#VBP,E-NP-singular],[,#,,O]China[China#NNP,B-NP-singular|E- NP-singular]has[have#VBZ,B-VP]increased[increase#VBN,I-VP]funds[fund#NNS, fund#VBZ,B-NP-plural|E-NP-plural]for[for#CC,for#IN,for#RP,B-PP]development [development#NN:UN,B-NP-singular]research[research#NN:U,research#VB,research# VBP,E-NP-singular]substantially[substantially#RB,B-ADVP]in[in#IN,in#NN,in#RP, B-PP]recent[recent#JJ,B-NP-plural]years[year#NNS,E-NP-plural].[.#.,<#S>,O]
<S>Universities[university#NNS,B-NP-plural]and[and#CC,I-NP-plural] research[research#NN:U,research#VB,research#VBP,I-NP-plural]institutions [institution#NNS,E-NP-plural]in[in#IN,B-PP]China[China#NNP,B-NP-singular|E- NP-singular]are[be#VBP,B-VP]actively[actively#RB,I-VP]carrying[carry#VBG,I- VP]out[out#IN,out#NN,out#RB,out#RP,out#UH,B-PRT]innovation[innovation#NN:UN, B-NP-plural]researches[research#NNS,research#VBZ,E-NP-plural],[,#,,O]which [which#WDT,B-NP-singular|E-NP-singular]cover[cover#NN:UN,cover#VB,cover#VBP, B-VP]high-technology[high-technology#JJ,B-NP-plural]fields[field#NNS,E-NP- plural]such[such#DT,B-PP]as[as#CC,as#IN,as#RB,I-PP]big[big#JJ,big#RB,B-NP- plural]data[data#NN:UN,datum#NNS,E-NP-plural],[,#,,O]biochemistry [biochemistry#NN:U,B-NP-singular|E-NP-singular],[,#,,O]new[new#JJ,B-NP- plural]energy[energy#NN:UN,I-NP-plural]and[and#CC,I-NP-plural]robots[robot# NNS,E-NP-plural],[,#,,O]etc[etc#null,B-NP-singular|E-NP-singular].[.#.,O]They [they#PRP,B-NP-singular|E-NP-singular]also[also#RB,B-ADVP]cooperate [cooperate#VB,cooperate#VBP,B-VP]with[with#IN,with#RP,B-PP]science[science# NN:UN,B-NP-singular]park[park#NN,park#VB,park#VBP,E-NP-singular]in[in#IN,in# NN,in#RP,B-PP]various[various#JJ,B-NP-plural]regions[region#NNS,E-NP-plural], [,#,,O]commercializing[commercialize#VBG,B-VP]the[the#DT,B-NP-plural]research [research#NN:U,I-NP-plural]results[result#NNS,result#VBZ,E-NP-plural]of[of# IN,B-PP]innovation[innovation#NN:UN,B-NP-singular|E-NP-singular].[.#.,<#S>,O]
<S>Meanwhile[meanwhile#JJ,meanwhile#NN:U,meanwhile#RB,B-ADVP],[,#,,O] no[no#DT,B-NP-singular]matter[matter#NN:UN,E-NP-singular]in[in#IN,B-PP] production[production#NN:UN,B-NP-singular]and[and#CC,I-NP-singular]business [business#JJ,I-NP-singular]model[model#JJ,model#NN,model#VB,model#VBP,E-NP- singular],[,#,,O]entrepreneurs[entrepreneur#NNS,B-NP-plural|E-NP-plural]in [in#IN,B-PP]China[China#NNP,B-NP-singular|E-NP-singular]are[be#VBP,B-VP] competing[compete#VBG,I-VP]to[to#IN,to#TO,I-VP]be[be#VB,I-VP]pioneers [pioneer#NNS,B-NP-plural|E-NP-plural]in[in#IN,B-PP]innovation[innovation#NN: UN,B-NP-singular|E-NP-singular]to[to#IN,to#TO,B-VP]adapt[adapt#VB,adapt#VBP, I-VP]to[to#IN,to#TO,B-PP]the[the#DT,B-NP-singular]constantly[constantly#RB,I- NP-singular]changing[change#VBG,E-NP-singular]and[and#CC,O]increasing [increasing#JJ,increasing#NN:UN,increase#VBG,B-VP]needs[need#NNS,B-NP-plural| E-NP-plural]of[of#IN,B-PP]the[the#DT,B-NP-singular]consumer[consumer#NN:UN,I- NP-singular]market[market#NN:UN,E-NP-singular]at[at#IN,at#RP,B-PP]home[home# NN:UN,B-NP-singular|E-NP-singular]and[and#CC,O]abroad[abroad#JJ,abroad#RB,B- ADVP].[.#.,<#S>,O]
Standard English translation part of speech disambiguation result：
<S>
China[China#NNP,china#JJ,china#NN:U,B-NP-singular|E-NP-singular]'['# POS]s[s#POS]innovation[innovation#NN:UN,E-NP-singular]is[be#VBZ,B-VP] flourishing[flourish#VBG,I-VP]faster[faster#RBR,fast#JJR,B-ADVP]than[than#IN, B-PP]ever[ever#JJ,ever#RB,ever#RP,B-ADVP]before[before#IN,before#RP,I-ADVP]. [.#.,<#S>,O]
<S>In[in#IN,B-PP]order[order#NN:UN,B-NP-singular|E-NP-singular]to[to# IN,to#TO,B-VP]surpass[surpass#VB,surpass#VBP,I-VP]developed[develop#VBD, develop#VBN,B-NP-plural]countries[country#NNS,E-NP-plural]on[on#IN,on#JJ,on# RP,B-PP]science[science#NN:UN,B-NP-singular]and[and#CC,I-NP-singular] technology[technology#NN:UN,E-NP-singular]as[as#RB,B-ADVP]soon[soon#JJ,soon# RB,I-ADVP]as[as#RB,B-PP]possible[possible#JJ,possible#NN,B-ADJP],[,#,,O]China [China#NNP,B-NP-singular|E-NP-singular]has[have#VBZ,B-VP]sharply[sharply#RB, I-VP]increased[increase#VBN,I-VP]research[research#NN:U,B-NP-singular]and [and#CC,I-NP-singular]development[development#NN:UN,I-NP-singular]fund[fund# NN:UN,fund#VB,fund#VBP,E-NP-singular].[.#.,<#S>,O]
<S>Chinese[Chinese#JJ,B-NP-plural]universities[university#NNS,I-NP- plural]and[and#CC,I-NP-plural]institutes[institute#NNS,E-NP-plural]are[be# VBP,B-VP]actively[actively#RB,I-VP]doing[doing#NN:UN,do#VBG,I-VP]innovative [innovative#JJ,B-NP-plural]researches[research#NNS,E-NP-plural],[,#,,O] covering[covering#JJ,covering#NN:UN,cover#VBG,B-VP]various[various#JJ,B-NP- plural]fields[field#NNS,E-NP-plural]of[of#IN,B-PP]high[high#JJ,high#NN,high# RP,B-NP-singular]technology[technology#NN:UN,E-NP-singular],[,#,,O]from[from# IN,from#RP,B-PP]big[big#JJ,big#RB,B-NP-plural]data[data#NN:UN,datum#NNS,E-NP- plural]to[to#IN,to#TO,B-VP]biochemistry[biochemistry#NN:U,I-VP],[,#,,O]and [and#CC,O]from[from#IN,from#RP,B-PP]new[new#JJ,B-NP-singular]energy[energy# NN:UN,E-NP-singular]to[to#IN,to#TO,B-PP]robots[robot#NNS,B-NP-plural|E-NP- plural].[.#.,<#S>,O]
<S>They[they#PRP,B-NP-singular|E-NP-singular]are[be#VBP,B-VP]also [also#RB,I-VP]cooperating[cooperate#VBG,I-VP]with[with#IN,with#RP,B-PP] science[science#NN:UN,B-NP-plural]and[and#CC,I-NP-plural]technology [technology#NN:UN,I-NP-plural]parks[park#NNS,park#VBZ,E-NP-plural]in[in#IN, in#NN,in#RP,B-PP]different[different#JJ,B-NP-plural]places[place#NNS,E-NP- plural],[,#,,O]so[so#RB,O]as[as#CC,as#IN,O]to[to#TO,B-VP]commercialize [commercialize#VB,I-VP]their[their#PRP$,B-NP-plural]fruits[fruit#NNS,E-NP- plural]of[of#IN,B-PP]innovation[innovation#NN:UN,B-NP-singular|E-NP- singular].[.#.,<#S>,O]
<S>In[in#IN,B-PP]the[the#DT,B-NP-singular]meantime[meantime#JJ, meantime#NN:U,E-NP-singular],[,#,,O]to[to#IN,to#TO,B-VP]adapt[adapt#VB,adapt# VBP,I-VP]to[to#IN,to#TO,B-PP]the[the#DT,B-NP-singular]changing[change#VBG,I- NP-singular]foreign[foreign#JJ,I-NP-singular]and[and#CC,I-NP-singular] domestic[domestic#JJ,I-NP-singular]market[market#NN:UN,E-NP-singular],[,#,,O] and[and#CC,O]to[to#TO,B-VP]satisfy[satisfy#VB,I-VP]the[the#DT,B-NP-singular] growing[growing#NN:UN,grow#VBG,I-NP-singular]demand[demand#NN:UN,E-NP- singular],[,#,,O]Chinese[Chinese#JJ,B-NP-plural]entrepreneurs[entrepreneur# NNS,E-NP-plural]are[be#VBP,B-VP]also[also#RB,I-VP]making[making#NN:UN,make# VBG,I-VP]pioneering[pioneer#VBG,B-NP-plural]efforts[effort#NNS,E-NP-plural]to [to#TO,B-VP]innovate[innovate#VB,I-VP]their[their#PRP$,B-NP-plural]products [product#NNS,I-NP-plural]and[and#CC,I-NP-plural]business[business#JJ,I-NP- plural]models[model#NNS,E-NP-plural].[.#.,<#S>,O]
Second step：Perform " translation English translation informativeness analysis module "
Translation English translation informativeness analysis module is the translation English translation and standard translation using first step generation The word segmentation result of English translation pretreatment module, then to translation English translation in word carry out accurately matching, stem matching, And synonym matching, and the successful word of record matching and its numbering, finally utilize train that English text collection generates accurate With weight, stem matching weight, synonym matching weight and translate the total words calculating translation that the match is successful in English translation The informativeness of English translation.The translation English translation informativeness analysis result of present embodiment is as follows：
Mark in translation English translation and translate correct English word：
Innovation is progressing in an unprecedented speed in China.In order to catch up with those developed countries in the world as fast as it can in the science and technology field,China has increased funds for development research substantially in recent years.Universities and research institutions in China are actively carrying out innovation researches,which cover high-technology fields such as big data,biochemistry,new energy and robots, etc.They also cooperate with science park in various regions,commercializing the research results of innovation.Meanwhile,no matter in production and business model,entrepreneurs in China are competing to be pioneers in innovation to adapt to the constantly changing and increasing needs of the consumer market at home and abroad.
Translate English translation informativeness：63 points.
Third step：Perform " translation English translation semantic similarity analysis module "
Translation English translation semantic similarity analysis module is translation English translation, the standard English using first step generation Language translation part of speech disambiguation result, searched according to part of speech disambiguation result and record word corresponding node in English Semantic dictionary and compiled Number, in English Semantic dictionary, found from node serial number corresponding to translation English translation word corresponding to standard English word Node serial number, the probability of directed edge between node serial number and node corresponding to each word is recorded on accessed path, according to looking into Look on path the probability of directed edge between node serial number and node corresponding to each word, generation translation English translation probability distribution One-dimensional vector and standard English translation probability distribution one-dimensional vector, according to translation English translation probability distribution one-dimensional vector and standard English translation probability distribution one-dimensional vector calculates and exports the semantic similarity of translation English translation.The translation English of present embodiment The semantic similarity analysis result of language translation is as follows：
Translate English translation probability distribution bivector：
Node serial number=2156 probable values=3.0124842E-4
Node serial number=4370 probable values=5.800353E-4
Node serial number=6845 probable values=6.6988135E-4
Node serial number=100600 probable values=2.8010816E-4
Node serial number=10177 probable values=2.602892E-4
Probable value=0.021831261 of node serial number=46299
Node serial number=15673 probable values=2.3386389E-4
Probable value=0.0012974822 of node serial number=105382
Node serial number=54185 probable values=2.3650641E-4
Node serial number=17634 probable values=5.919267E-4
Probable value=0.00863843 of node serial number=20446
Node serial number=14347 probable values=4.175197E-4
Node serial number=20828 probable values=6.5931125E-4
Node serial number=19141 probable values=2.867145E-4
Node serial number=26691 probable values=9.737723E-4
Probable value=0.0026993442 of node serial number=102174
Node serial number=28336 probable values=2.3122136E-4
Node serial number=29957 probable values=7.412297E-4
Node serial number=22319 probable values=2.4179148E-4
Probable value=0.0014851018 of node serial number=43079
Probable value=0.00199511 of node serial number=32419
Node serial number=35642 probable values=2.8142944E-4
Node serial number=4743 probable values=3.0917602E-4
Node serial number=40963 probable values=6.0513936E-4
Node serial number=89379 probable values=2.4311275E-4
Node serial number=44605 probable values=2.668955E-4
Node serial number=55839 probable values=3.0124842E-4
Node serial number=46581 probable values=4.294111E-4
Node serial number=48312 probable values=2.906783E-4
Probable value=0.018896732 of node serial number=52569
Node serial number=53413 probable values=4.294111E-4
Node serial number=97348 probable values=4.2544733E-4
Probable value=0.002328069 of node serial number=41450
Node serial number=58665 probable values=2.8142944E-4
Node serial number=30269 probable values=3.052122E-4
Node serial number=59301 probable values=5.1132956E-4
Node serial number=62168 probable values=4.2676856E-4
Node serial number=62590 probable values=6.276009E-4
Node serial number=65360 probable values=2.4707653E-4
Probable value=0.002806367 of node serial number=6306
Node serial number=67688 probable values=3.4881395E-4
Node serial number=19078 probable values=4.6640655E-4
Node serial number=71778 probable values=3.5013523E-4
Node serial number=72009 probable values=4.2676856E-4
Node serial number=6295 probable values=3.3428005E-4
Node serial number=19068 probable values=2.3386389E-4
Node serial number=113288 probable values=4.928318E-4
Node serial number=43019 probable values=2.4179148E-4
Probable value=0.001411111 of node serial number=81309
Node serial number=81657 probable values=7.346233E-4
……
Standard English translation probability distribution bivector：
Node serial number=2200 probable values=2.9596334E-4
Node serial number=98019 probable values=5.4436113E-4
Node serial number=59686 probable values=9.3413435E-4
Probable value=0.0016846128 of node serial number=67669
Node serial number=34130 probable values=3.4881395E-4
Probable value=0.0010200165 of node serial number=91621
Node serial number=75648 probable values=3.7788178E-4
Node serial number=46900 probable values=6.5931125E-4
Node serial number=43701 probable values=3.1578232E-4
Node serial number=101190 probable values=5.813566E-4
Node serial number=30921 probable values=3.8977317E-4
Probable value=0.0020875987 of node serial number=45290
Node serial number=109167 probable values=6.540261E-4
Node serial number=66046 probable values=6.196732E-4
Node serial number=107567 probable values=5.602163E-4
Node serial number=46880 probable values=3.1049727E-4
Node serial number=53598 probable values=2.5500412E-4
Probable value=0.0020572094 of node serial number=104364
Node serial number=38884 probable values=2.3386389E-4
Node serial number=10136 probable values=4.1619848E-4
Node serial number=78469 probable values=3.9241568E-4
Probable value=0.0011851747 of node serial number=56445
Node serial number=58041 probable values=4.1619848E-4
Node serial number=42070 probable values=4.716916E-4
Node serial number=13323 probable values=3.0124842E-4
Node serial number=115514 probable values=2.9332083E-4
Node serial number=62812 probable values=3.7523927E-4
Node serial number=53222 probable values=2.4839782E-4
Node serial number=45236 probable values=4.18841E-4
Node serial number=24464 probable values=2.6293172E-4
Probable value=0.002129879 of node serial number=101119
Node serial number=97923 probable values=4.0827086E-4
Probable value=0.001301446 of node serial number=86740
Node serial number=73962 probable values=5.1265076E-4
Node serial number=80341 probable values=2.8803575E-4
Probable value=0.0033599767 of node serial number=32429
Node serial number=112272 probable values=4.0430707E-4
Probable value=0.0010358717 of node serial number=46792
Node serial number=30814 probable values=3.567415E-4
Probable value=0.0028195793 of node serial number=61155
Node serial number=37190 probable values=5.958905E-4
Probable value=0.002388847 of node serial number=91486
Probable value=0.0017638888 of node serial number=43574
Node serial number=24409 probable values=3.1974612E-4
Node serial number=61139 probable values=5.6946516E-4
Node serial number=59537 probable values=6.196732E-4
Node serial number=67519 probable values=3.54099E-4
Node serial number=61129 probable values=2.4707653E-4
Node serial number=88276 probable values=2.906783E-4
Node serial number=439 probable values=3.4485015E-4
……
Translate English translation semantic similarity：78.5 points.
Four steps：Perform " translation English translation quality analysis results generation module "
Translation English translation quality analysis results generation module is that the translation English translation of comprehensive second step output is loyal Analysis result, the translation English translation semantic similarity analysis result of third step output are spent, generates and exports translation English and translate Literary mass fraction and comment.The English translation quality analysis results generation form of present embodiment is as follows：
Translate English translation mass fraction：70.8 points.
Translate English translation quality comment：English translation and being consistent property of standard English translation are good, can correctly express original The text meaning.

Claims (8)

1. a kind of translator of Chinese into English English translation mass analysis method, it is characterized in that：Including one by being linked in sequence Translate English translation pretreatment module, translation English translation informativeness analysis module, the semantic similarity analysis of translation English translation The analysis model of module, translation English translation quality analysis results generation module composition, its analysis method comprise the following steps：
(1) English translation pretreatment module input translation English translation and standard English translation are translated, to translation English translation and Standard English translation is segmented respectively, word small letter, removal stop words；To participle, word small letter, remove stop words Translate English translation and standard English translation carries out part-of-speech tagging, part of speech disambiguation, phrase stripping and slicing；Export turning over for above-mentioned two-step pretreatment Translate the pre-processed results of English translation and standard English translation；
(2) it is pre- that English translation informativeness analysis module input translation English translation, standard English translation, translation English translation are translated Result, standard English translation pre-processed results, numbering translation English translation, standard English translation, translation English translation are pre- Word in result, standard English translation pre-processed results；To the word and standard English translation in translation English translation In word carry out accurate matching and the successful word of record matching and its numbering；To translation English translation and standard English translation Accurately match remaining word and carry out stem matching and the successful word of record matching and its numbering；To translation English translation and mark Quasi- English translation stem matches remaining word and carries out synonym matching and the successful word of record matching and its numbering；5th, Translated using the word matched rate accuracy rate weight and translation English of training translation English translation collection and the generation of standard English collection of translations The total words that the match is successful in text calculate and export the informativeness of translation English translation；
(3) English translation semantic similarity analysis module input translation English translation, standard English translation part of speech disambiguation knot are translated Fruit；Searched according to part of speech disambiguation result and record word corresponding node serial number in English Semantic dictionary；In English Semantic word In allusion quotation, from translation English translation word corresponding to node serial number find standard English word corresponding to node serial number, record On accessed path corresponding to each word between node serial number and node directed edge probability；According to each word on accessed path Word probability distribution bivector in English translation is translated in the probability of directed edge between corresponding node serial number and node, generation, And iterate to calculate out word probability in standard English translation and be distributed bivector；Merge word probability in translation English translation to be distributed Bivector obtains translating English translation probability distribution bivector, and is carried from translation English translation probability distribution bivector Take word probability value, generation translation English translation probability distribution one-dimensional vector；Merge standard English translation word probability distribution two Dimensional vector obtains standard English translation probability distribution bivector, and is extracted from standard English translation probability distribution bivector Word probability value, generate standard English translation probability distribution one-dimensional vector；According to translation English translation probability distribution one-dimensional vector Calculated with standard English translation probability distribution one-dimensional vector and export translation English translation semantic similarity；
(4) the output knot of English translation quality analysis results generation module input translation English translation informativeness analysis module is translated Fruit, the output result for translating English translation semantic similarity analysis module；According to translation English translation informativeness analysis module Output result, translate English translation semantic similarity analysis module output result generation translation English translation mass fraction and Comment.
2. analysis method according to claim 1, it is characterized in that：Described translation English translation pretreatment module processing step It is rapid as follows：
P201 starts；
P202 reads in translation English translation and standard English translation；
P203 judges to translate English translation and whether standard English translation quantity is identical, if it is turns P204, otherwise turns P202；
P204 is to translation English translation participle and will translate the word small letter after English translation segments；
The word segmentation result of P205 output translation English translations and the word small letter result of translation English translation；
P206 statistical translation English translation total words；
P207 carries out part-of-speech tagging to the word segmentation result for translating English translation；
P208 carries out phrase stripping and slicing to the part-of-speech tagging result for translating English translation；
P209 carries out part of speech disambiguation to the phrase stripping and slicing result for translating English translation；
The part-of-speech tagging result of P210 output translation English translations, the part of speech disambiguation result for translating English translation, translation English are translated The phrase stripping and slicing result of text；
P211 to standard English translation segment and by standard English translation segment after word small letter；
The word segmentation result of P212 outputting standard English translations and the word small letter result of standard English translation；
P213 SSs English batch translation total words；
P214 carries out part-of-speech tagging to the word segmentation result of standard English translation；
P215 carries out phrase stripping and slicing to the part-of-speech tagging result of standard English translation；
Phrase stripping and slicing results of the P216 to standard English translation carries out part of speech disambiguation；
The part-of-speech tagging result of P217 outputting standard English translations, the part of speech disambiguation result of standard English translation, standard English are translated The phrase stripping and slicing result of text；
P218 terminates.
3. analysis method according to claim 1, it is characterized in that：Described translation English translation informativeness analysis module Calculation formula is defined as follows：
(1) word matched accuracy rate calculation formula
Word matched accuracy rate refers to translate word matched success number and translation English between English translation and standard English translation The ratio of total words in language translation, its calculation formula are as follows：
(2) word matched recall rate calculation formula
Word matched recall rate refers to translate word matched success number and standard English between English translation and standard English translation The ratio of total words in language translation, its calculation formula are as follows：
(3) harmonic-mean calculation formula
Harmonic-mean refer to the word matched accuracy rate of calculation formula (1) and calculation formula (2) word matched recall rate it Between average value, its calculation formula is as follows：
In calculation formula (3), word matched success rate is calculated by calculation formula (1), and word matched recall rate is by calculating Formula (2) is calculated, and a is the weight of word matched accuracy rate, and 1-a is the weight of word matched recall rate.
(4) penalty coefficient value calculation formula
Penalty coefficient value is to reduce to miss between translation English translation content and standard English translation content caused by harmonic-mean The coefficient of difference, its calculation formula are as follows：
In calculation formula (4), phrase number refers to translate the sum of noun phrase and verb phrase in English translation；B is punishment The adjusting parameter of coefficient value size；C is phrase number and the adjustment of the word number ratio size that the match is successful in translation English translation Parameter；B and c is calculated by translation English translation informativeness analysis module.
(5) English translation informativeness calculation formula is translated
Translation English translation informativeness refers to semanteme of word similarity degree of the word with standard English translation for translating English translation, Its calculation formula is as follows：
Translate English translation informativeness=(1- penalty coefficients value) × harmonic-mean (5)
In calculation formula (5), harmonic average numerical value is calculated by calculation formula (3), and penalty coefficient value is by calculation formula (4) It is calculated.
4. analysis method according to claim 1, it is characterized in that：At described translation English translation informativeness analysis module It is as follows to manage step：
P301 starts；
P302 reads in the word segmentation result of translation English translation and the word small letter result of translation English translation；
P303 reads in the word segmentation result of standard English translation and the word small letter result of standard English translation；
P304 is according to the word segmentation result of translation English translation and word small letter result, the standard English translation of translation English translation Word segmentation result and standard English translation word small letter result, generation translation English translation-standard English translation text It is right；
P305 reads in the text pair of one group of translation English translation-standard English translation；
P306 starts from scratch the text pair translation English translation each word for translating English translation-standard English translation volume Number；
P307 starts from scratch the text pair standard English translation each word for translating English translation-standard English translation volume Number；
The text pair for translating English translation-standard English translation is translated each word of English translation, and translation English by P308 The each word of standard English translation of the text pair of language translation-standard English translation is accurately matched；
The text pair translation English translation of P309 statistical translations English translation-standard English translation, and translation English translation- The accurate word that the match is successful of the text pair standard English translation of standard English translation and numbering；
P310 judges whether the text pair translation English translation for translating English translation-standard English translation also has inaccurate matching Word, if it is turn P308, otherwise turn P311；
The text pair for translating English translation-standard English translation is translated English translation, and translation English translation-mark by P311 The text pair standard English translation of quasi- English translation carries out stemmed without the accurate word that the match is successful；
Word in the text pair translation English translation of translation English translation-standard English translation after P312 will be stemmed, and The word of the text pair standard English translation of translation English translation-standard English translation after stemmed carries out stem matching；
The text pair translation English translation of P313 statistical translations English translation-standard English translation, and translation English translation- The text pair standard English translation stem word that the match is successful and numbering of standard English translation；
P314 judges whether the text pair for translating English translation-standard English translation is translated also not stemmed in English translation The word of matching, if it is turn P312, otherwise turn P315；
The text pair for translating English translation-standard English translation is translated English translation, and translation English translation-mark by P315 The text pair standard English translation of quasi- English translation does not have the stem word that the match is successful to carry out meaning of a word lexical or textual analysis；
P316 translates the text pair that English translation-standard English translation is translated after the meaning of a word lexical or textual analysis word of English translation, And after meaning of a word lexical or textual analysis translate English translation-standard English translation text pair standard English translation word carry out it is synonymous Word matches；
The text pair translation English translation of P317 statistical translations English translation-standard English translation, and translation English translation- The text pair standard English translation synonym word that the match is successful and numbering of standard English translation；
P318 judges that the text pair for translating English translation-standard English translation translates in English translation whether also have non-synonym The word of matching, if it is turn P315, otherwise turn P319；
P319 judges whether the text pair for the translation English translation-standard English translation do not corrected also, if it is turns P305, Otherwise P320 is turned；
P320 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation English The result that the text pair standard English translation word of translation-standard English translation accurately matches；
P321 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation English The result of the text pair standard English translation word stem matching of translation-standard English translation；
P322 reads in the text pair translation English translation word of translation English translation-standard English translation, and translation English The result of the text pair standard English translation word synonym matching of translation-standard English translation；
P323 marks the text pair translation English translation word that the match is successful of translation English translation-standard English translation；
P324 reads in the text pair translation English translation total words of translation English translation-standard English translation；
P325 reads in the text pair standard English translation total words of translation English translation-standard English translation；
P326 translates the text pair for translating English translation-standard English translation the total words of English translation, and translation Total words in the text pair standard English translation of English translation-standard English translation, translation English translation-standard English are translated The text pair translation English translation of text accurately matches, stem matches, the synonym total words that the match is successful, substitutes into translation English Calculation formula (1), the calculation formula (2) of language translation informativeness analysis module calculate translation English translation-standard English translation Text pair translates the accuracy rate and recall rate of English translation word matched；
P327 is using translating English translation collection and the training of standard English collection of translations draws optimized parameter a, b, c value；
The text pair of a value, translation English translation-standard English translation is translated the accurate of English translation word matched by P328 Rate, the recall rate for the text pair translation English translation word matched for translating English translation-standard English translation substitute into translation English The calculation formula (3) of language translation informativeness analysis module calculates harmonic-mean；
The value of parameter b, c, the text pair of translation English translation-standard English translation are translated the number of English translation phrase by P329 Amount, translation English translation-standard English translation text pair translation English translation accurately match, stem matching, synonym With successful total words, the calculation formula (4) for substituting into translation English translation informativeness analysis module calculates penalty coefficient；
By harmonic-mean, the calculation formula (5) that penalty coefficient substitutes into translation English translation informativeness analysis module calculates P330 Translate English translation informativeness；
P331 terminates.
5. analysis method according to claim 1, it is characterized in that：Described translation English translation semantic similarity analysis mould The calculation formula of block is defined as follows：
(1) ProbabilityDistribution Vector calculation formula
ProbabilityDistribution Vector refers in English Semantic dictionary, from node checks standard English corresponding to translation English translation word Node corresponding to word, on accessed path corresponding to each word between node serial number and node the probability of directed edge form to Amount, its calculation formula are as follows：
ProbabilityDistribution Vectort=(1-a) × adjacency matrix × ProbabilityDistribution Vectort-1+ a × ProbabilityDistribution Vector0 (6)
In calculation formula (6), t represents the number searched, and a represents the probability of directed edge on the t times accessed path, adjacency matrix Represent the word adjacency matrix in English Semantic dictionary, ProbabilityDistribution Vector0Represent the probability distribution of lookup original position node Vector, ProbabilityDistribution Vectort-1Represent the ProbabilityDistribution Vector of the t-1 times lookup node, ProbabilityDistribution VectortRepresent to look into for the t times Look for the ProbabilityDistribution Vector of node.
(2) English translation Semantic Similarity Measurement formula is translated
Translation English translation semantic similarity refers to translate semantic similar between English translation content and standard English translation content Degree, its calculation formula are as follows：
In calculation formula (7), translation English translation probability distribution one-dimensional vector refers in English Semantic dictionary, from translation English Node serial number corresponding to language translation word searches node serial number corresponding to standard English word, and each word is corresponding on accessed path Node between directed edge probability form one-dimensional vector；Standard English translation probability distribution one-dimensional vector refers in English language In adopted dictionary, node serial number corresponding to standard English word is searched from node serial number corresponding to translation English translation word, is searched The one-dimensional vector that the probability of the directed edge of path end position node connection is formed；| | translation English translation probability distribution it is one-dimensional to Amount | | refer to the mould for translating English translation probability distribution one-dimensional vector；| | standard English translation probability distribution one-dimensional vector | | refer to The mould of standard English translation probability distribution one-dimensional vector.
6. analysis method according to claim 1, it is characterized in that：Described translation English translation semantic similarity analysis mould Block processing step is as follows：
P401 starts；
P402 reads translation English translation word part of speech disambiguation result；
P403 ergodic translation English translation word part of speech disambiguation results；
P404 according to translation English translation word part of speech disambiguation result search and record word in English Semantic dictionary corresponding to Node serial number；
P405 judges whether the translation English translation word part of speech disambiguation result not traveled through also, if it is turns P403, otherwise Turn P406；
P406 reads standard English translation word part of speech disambiguation result；
P407 travels through standard English translation word part of speech disambiguation result；
P408 according to standard English translation word part of speech disambiguation result search and record word in English Semantic dictionary corresponding to Node serial number；
P409 judges whether the standard English translation word part of speech disambiguation result not traveled through also, if it is turns P407, otherwise Turn P410；
It is corresponding that P410 searches standard English word in English Semantic dictionary, from node serial number corresponding to translation English translation word Node serial number；
P411 records on accessed path the probability of directed edge between node serial number and node corresponding to each word；
The probability of P412 directed edges between node serial number and node according to corresponding to each word on accessed path, generation translation English Language translation word probability is distributed bivector；
P413 calculates single in standard English translation according to the calculation formula (6) of translation English translation semantic similarity analysis module Word probability is distributed bivector；
P414 judges to translate in English translation whether also have the word do not searched, if it is turns P410, otherwise turns P415；
P415 merges translation English translation word probability distribution bivector and obtains translating English translation probability distribution bivector；
P416 extracts word probability value, generation translation English translation probability point from translation English translation probability distribution bivector Cloth one-dimensional vector；
P417 merges standard English translation word probability distribution bivector and obtains standard English translation probability distribution bivector；
P418 extracts word probability value, generation standard English translation probability point from standard English translation probability distribution bivector Cloth one-dimensional vector；
P419 is calculated according to the calculation formula (7) of translation English translation semantic similarity analysis module and is exported translation English translation Semantic similarity；
P420 terminates.
7. analysis method according to claim 1, it is characterized in that：Described translation English translation quality analysis results generation The calculation formula of module is defined as follows：
(1) English translation mass fraction calculation formula is translated
Translation English translation mass fraction refers to the content journey similar to the contents semantic of standard English translation for translating English translation Degree, its calculation formula are as follows：
English translation mass fraction=0.5 × English translation+0.5 × English translation of informativeness semantic similarity (8)
In calculation formula (8), translation English translation informativeness score is calculated by calculation formula (5), translates English translation Semantic similarity is calculated by calculation formula (7).
8. analysis method according to claim 1, it is characterized in that：Described translation English translation quality analysis results generation Resume module step is as follows：
P501 starts；
P502 reads translation English translation informativeness；
P503 reads translation English translation semantic similarity；
P504 is calculated according to the calculation formula (8) of translation English translation quality analysis results generation module and is exported translation English and translates Literary mass fraction；
P505 is according to translation English translation mass fraction output translation English translation comment；
P506 terminates.
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