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.
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.