CN110046229A - For obtaining the method and device of information - Google Patents
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- CN110046229A CN110046229A CN201910312438.6A CN201910312438A CN110046229A CN 110046229 A CN110046229 A CN 110046229A CN 201910312438 A CN201910312438 A CN 201910312438A CN 110046229 A CN110046229 A CN 110046229A
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Abstract
Embodiment of the disclosure discloses the method and device for obtaining information.One specific embodiment of this method includes: that at least one characteristic information is extracted from information to be processed, wherein characteristic information is used to characterize the information type of above-mentioned information to be processed；Above-mentioned at least one characteristic information is imported to information identification model trained in advance, obtains the level information of above-mentioned information to be processed, above- mentioned information identification model is corresponding with above- mentioned information type, for the corresponding relationship between characteristic feature information and level information；Result information is constructed according to above-mentioned level information.The embodiment is conducive to judge the safety of information to be processed.
Description
Technical field
Embodiment of the disclosure is related to technical field of data processing, and in particular to for obtaining the method and device of information.
Background technique
With the development of society governed by law, the legal consciousness of people is also improved.People are in work and life through common
To Miscellaneous Documents relevant to law.For example, the employment contract signed with company, the contract of renting a house etc. signed with landlord.
Summary of the invention
Embodiment of the disclosure proposes the method and device for obtaining information.
In a first aspect, embodiment of the disclosure provides a kind of method for obtaining information, this method comprises: to from
At least one characteristic information is extracted in reason information, wherein features described above information is used to characterize the info class of above-mentioned information to be processed
Type；Above-mentioned at least one characteristic information is imported to information identification model trained in advance, obtains the rank of above-mentioned information to be processed
Information；Wherein, above- mentioned information identification model is corresponding with above- mentioned information type, between characteristic feature information and level information
Corresponding relationship；And result information is constructed according to above-mentioned level information.
It is in some embodiments, above-mentioned that above-mentioned at least one characteristic information is imported to information identification model trained in advance,
Obtain the level information of above-mentioned information to be processed, comprising: inquire the information identification model of corresponding above- mentioned information type.
In some embodiments, above- mentioned information identification model is obtained by following steps training: obtaining specify information type
A plurality of sample information and corresponding above-mentioned a plurality of sample information in every sample information sample level information, above-mentioned sample information
Including at least one sample characteristics information；Using above-mentioned a plurality of sample information as input, by in above-mentioned a plurality of sample information
For the corresponding sample level information of every sample information as output, training obtains corresponding to the information identification of above-mentioned specify information type
Model.
In some embodiments, it is above-mentioned using above-mentioned a plurality of sample information as input, will in above-mentioned a plurality of sample information
The corresponding sample level information of every sample information as output, the information that training obtains corresponding to above-mentioned specify information type is known
Other model, comprising: execute following training step: every sample information in above-mentioned a plurality of sample information is sequentially input to initial
Information identification model obtains predicted level information corresponding to every sample information in above-mentioned a plurality of sample information, will be above-mentioned
Above-mentioned sample corresponding to predicted level information corresponding to every sample information in a plurality of sample information and the sample information
Level information is compared, and is obtained the predictablity rate of above-mentioned initial information identification model, whether is determined above-mentioned predictablity rate
Greater than default accuracy rate threshold value, if more than above-mentioned default accuracy rate threshold value, then using above-mentioned initial information identification model as training
The information identification model of completion.
In some embodiments, it is above-mentioned using above-mentioned a plurality of sample information as input, will in above-mentioned a plurality of sample information
The corresponding sample level information of every sample information as output, the information that training obtains corresponding to above-mentioned specify information type is known
Other model, further includes: in response to adjusting the parameter of above-mentioned initial information identification model no more than above-mentioned default accuracy rate threshold value,
And continue to execute above-mentioned training step.
In some embodiments, above-mentioned sample level information is obtained by following steps: being mentioned from each sample information
Take at least one sample characteristics information；For the sample characteristics information in above-mentioned at least one sample characteristics information, the sample is obtained
The corresponding reference information of eigen information, said reference information are used for the rank degree of establishment numerical value to the sample characteristics information；
It is corresponding sample characteristics information degree of establishment numerical value by said reference information, obtains the sample rank letter of corresponding sample information
Breath.
In some embodiments, said reference information includes at least one rank setting information and weighted value, and, it is above-mentioned
It is corresponding sample characteristics information degree of establishment numerical value by said reference information, comprising: set by above-mentioned at least one rank
Rank setting information in confidence breath is that initial level numerical value is arranged in corresponding sample characteristics information, and passes through initial level numerical value
Weighted value with corresponding initial level numerical value is that final rating value is arranged in sample characteristics information.
In some embodiments, the above method further include: obtain and send corresponding the above results information at least one goes through
History reference information, wherein above-mentioned history reference information includes historical information corresponding with the above results information.
In some embodiments, the above method further include: in response to above-mentioned level information be less than setting rank, from it is above-mentioned to
Extract and show that at least one mark information, above-mentioned mark information are so that above-mentioned level information is less than in a few characteristic information
Set the characteristic information of rank.
In some embodiments, above- mentioned information type includes at least one of the following: legal documents, security information.
Second aspect, embodiment of the disclosure provide it is a kind of for obtaining the device of information, the device include: feature letter
Extraction unit is ceased, is configured to extract at least one characteristic information from information to be processed, wherein features described above information is used for table
Levy the information type of above-mentioned information to be processed；Level information acquiring unit is configured to lead above-mentioned at least one characteristic information
Enter in advance trained information identification model, obtain the level information of above-mentioned information to be processed, wherein above- mentioned information identification model with
Above- mentioned information type is corresponding, for the corresponding relationship between characteristic feature information and level information；Result information construction unit, quilt
It is configured to construct result information according to above-mentioned level information.
In some embodiments, above-mentioned level information acquiring unit includes: information identification model inquiry subelement, is configured
At the information identification model for inquiring corresponding above- mentioned information type.
In some embodiments, above-mentioned apparatus further includes information identification model training unit, is configured to training information knowledge
Other model, above- mentioned information identification model training unit include: acquisition of information subelement, are configured to obtain specify information type
The sample level information of every sample information, above-mentioned sample information packet in a plurality of sample information and corresponding above-mentioned a plurality of sample information
Include at least one sample characteristics information；Information identification model train subelement, be configured to using above-mentioned a plurality of sample information as
Input, using sample level information corresponding with every sample information in above-mentioned a plurality of sample information as output, training is obtained
The information identification model of corresponding above-mentioned specify information type.
In some embodiments, above- mentioned information identification model training subelement includes: information identification model training module, quilt
It is configured to sequentially input every sample information in above-mentioned a plurality of sample information to initial information identification model, obtain above-mentioned more
Predicted level information corresponding to every sample information in sample information, by every sample in above-mentioned a plurality of sample information
Predicted level information corresponding to information is compared with above-mentioned sample level information corresponding to the sample information, is obtained above-mentioned
The predictablity rate of initial information identification model, determines whether above-mentioned predictablity rate is greater than default accuracy rate threshold value, if more than
Above-mentioned default accuracy rate threshold value, then the information identification model completed above-mentioned initial information identification model as training.
In some embodiments, above- mentioned information identification model training subelement further include: parameter adjustment module, in response to not
It greater than above-mentioned default accuracy rate threshold value, is configured to adjust the parameter of above-mentioned initial information identification model, and continues to execute above-mentioned
Training step.
In some embodiments, above-mentioned apparatus further includes sample level information acquiring unit, is configured to obtain sample grade
Other information, above-mentioned sample level information acquiring unit include: sample characteristics information extraction subelement, are configured to from each galley proof
At least one sample characteristics information is extracted in this information；Reference information obtains subelement, for above-mentioned at least one sample characteristics
Sample characteristics information in information, is configured to obtain the corresponding reference information of sample characteristics information, and said reference information is used
In the rank degree of establishment numerical value to the sample characteristics information；Sample level information obtains subelement, is configured to by above-mentioned
Reference information is corresponding sample characteristics information degree of establishment numerical value, obtains the sample level information of corresponding sample information.
In some embodiments, said reference information includes at least one rank setting information and weighted value, and, it is above-mentioned
It includes: that sample level information obtains module that sample level information, which obtains subelement, is configured to through above-mentioned at least one rank
Rank setting information in setting information is that initial level numerical value is arranged in corresponding sample characteristics information, and passes through initial level number
The weighted value of value and corresponding initial level numerical value is that final rating value is arranged in sample characteristics information.
In some embodiments, above-mentioned apparatus further include: history reference information transmitting unit is configured to obtain and send
At least one history reference information of corresponding the above results information, wherein above-mentioned history reference information includes believing with the above results
Cease corresponding historical information.
In some embodiments, above-mentioned apparatus further include: prompt unit is less than setting grade in response to above-mentioned level information
Not, it is configured to extract and show from above-mentioned at least one characteristic information that at least one mark information, above-mentioned mark information are
So that above-mentioned level information is less than the characteristic information of setting rank.
In some embodiments, above- mentioned information type includes at least one of the following: legal documents, security information.
The third aspect, embodiment of the disclosure provide a kind of electronic equipment, comprising: one or more processors；Storage
Device is stored thereon with one or more programs, when said one or multiple programs are executed by said one or multiple processors,
So that said one or multiple processors execute the method for obtaining information of above-mentioned first aspect.
Fourth aspect, embodiment of the disclosure provide a kind of computer-readable medium, are stored thereon with computer program,
It is characterized in that, the program realizes the method for obtaining information of above-mentioned first aspect when being executed by processor.
The method and device for being used to obtain information that embodiment of the disclosure provides is extracted special first from information to be processed
Characteristic information, is then imported information identification model trained in advance, obtains the level information of information to be processed by reference breath；Finally
Result information is constructed according to level information.Technical scheme can obtain corresponding to the result information of information to be processed, favorably
It is judged in the safety to information to be processed.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the disclosure is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is that one embodiment of the disclosure can be applied to exemplary system architecture figure therein；
Fig. 2 is the flow chart according to one embodiment of the method for obtaining information of the disclosure；
Fig. 3 is the schematic diagram according to an application scenarios of the method for obtaining information of the disclosure；
Fig. 4 is the flow chart according to one embodiment of the information identification model training method of the disclosure；
Fig. 5 is the structural schematic diagram according to one embodiment of the device for obtaining information of the disclosure；
Fig. 6 is adapted for the electronic devices structure schematic diagram for realizing embodiment of the disclosure.
Specific embodiment
The disclosure is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the disclosure can phase
Mutually combination.The disclosure is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 is shown can the method for obtaining information using embodiment of the disclosure or the dress for obtaining information
The exemplary system architecture 100 set.
As shown in Figure 1, system architecture 100 may include terminal device 101,102,103, network 104 and server 105.
Network 104 between terminal device 101,102,103 and server 105 to provide the medium of communication link.Network 104 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to receive or send out
Send message etc..Various telecommunication customer end applications can be installed, such as web browser is answered on terminal device 101,102,103
With, shopping class application, searching class application, instant messaging tools, mailbox client, social platform software etc..
Terminal device 101,102,103 can be hardware, be also possible to software.When terminal device 101,102,103 is hard
When part, the various electronic equipments of information processing, including but not limited to smart phone, plate are can be with display screen and supported
Computer, pocket computer on knee and desktop computer etc..When terminal device 101,102,103 is software, can install
In above-mentioned cited electronic equipment.Multiple softwares or software module may be implemented into (such as providing distributed clothes in it
Business), single software or software module also may be implemented into, be not specifically limited herein.
Server 105 can be to provide the server of various services, for example, to terminal device 101,102,103 send to
Handle the server that information carries out data processing.Server can carry out the data such as the information to be processed received analyzing etc.
Reason, and processing result (such as result information) is fed back into terminal device.
It should be noted that can be by terminal device for obtaining the method for information provided by embodiment of the disclosure
101, it 102,103 is individually performed, or can also be executed jointly by terminal device 101,102,103 and server 105.Accordingly
Ground, the device for obtaining information can be set in terminal device 101,102,103, also can be set in server 105.
It should be noted that server can be hardware, it is also possible to software.When server is hardware, may be implemented
At the distributed server cluster that multiple servers form, individual server also may be implemented into.It, can when server is software
To be implemented as multiple softwares or software module (such as providing Distributed Services), single software or software also may be implemented into
Module is not specifically limited herein.
It should be understood that the number of terminal device, network and server in Fig. 1 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
With continued reference to Fig. 2, the process of one embodiment of the method for obtaining information according to the disclosure is shown
200.This be used for obtain information method the following steps are included:
Step 201, at least one characteristic information is extracted from information to be processed.
In the present embodiment, for obtain the method for information executing subject (such as terminal device shown in FIG. 1 101,
102,103 and/or server 105) letter can be carried out using it from user by wired connection mode or radio connection
The terminal of breath processing receives information to be processed.Wherein, information to be processed can be the data formats such as picture, text.Letter to be processed
Breath can be the content relevant to articles of law or regulation such as contract, agreement.It should be pointed out that above-mentioned radio connection can
To include but is not limited to 3G/4G connection, WiFi connection, bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra
Wideband) connection and other currently known or exploitation in the future radio connections.
In daily life, people can handle various types of information.Wherein, some information need to meet relevant form and want
It asks and/or content requires.For this purpose, can be with existing historical information in reference data/document databse, to use for reference historical information to working as
Preceding information performs corresponding processing.Wherein, information type may include legal documents and security information.More specifically, believe
Breath type can be various contracts etc., and then can be judged according to the articles of law that all kinds of contracts are related to information.Information
Type still can be labour contract, deal contract, contract of lease of property etc..For the people for not having legal profession knowledge, usually
Can not accuracy to legal documents and legal effect etc. make accurate judgement.Therefore, it is necessary to from the information in legal documents
In selection identify unreasonable place.It does not meet legal compulsory standardization when existing in legal documents or has unfavorable to one's party
Clause when, these legal documents may bring unnecessary risk.
The executing subject of the application can extract at least one after getting information to be processed from information to be processed first
Characteristic information.Wherein, characteristic information can be used for characterizing above-mentioned information to be processed information type it is different types of to be processed
The characteristic information that information includes is different, and characteristic information can be key word information corresponding to information type.For example, labour contract
Corresponding characteristic information may include: " labour contract ", " employee ", " company ", " trial period ", " dismissing " etc.；Deal contract pair
The characteristic information answered may include: " deal contract ", " deposit ", " penalty " etc..
Step 202, above-mentioned at least one characteristic information is imported to information identification model trained in advance, is obtained above-mentioned wait locate
Manage the level information of information.
In this step, characteristic information obtained above can be imported information trained in advance and identify mould by executing subject
Type, and then available level information.Wherein, information identification model can be corresponding with above- mentioned information type, is used for characteristic feature
Corresponding relationship between information and level information.
It is above-mentioned that above-mentioned at least one characteristic information is imported into instruction in advance in some optional implementations of the present embodiment
Experienced information identification model obtains the level information of above-mentioned information to be processed, may include: to inquire corresponding above- mentioned information type
Information identification model.
In practice, information to be processed can be a variety of different information types.For different information types, corresponding method
Restraining content can be different.Therefore, after getting characteristic information, executing subject is it needs to be determined that letter corresponding to these characteristic informations
Cease identification model.Executing subject can select corresponding information identification model by the corresponding information type of characteristic information.
In some optional implementations of the present embodiment, information identification model can be trained by following steps
It arrives:
The first step obtains every sample in a plurality of sample information and corresponding above-mentioned a plurality of sample information of specify information type
The sample level information of information.
For training information identification model, a plurality of sample information and correspondence of the available specify information type of executing subject
The sample level information of every sample information in above-mentioned a plurality of sample information.Wherein, sample level information can be technical staff
The rating information etc. that sample information is made according to the actual situation.
Second step will be believed using above-mentioned a plurality of sample information as input with every sample in above-mentioned a plurality of sample information
Corresponding sample level information is ceased as output, and training obtains corresponding to the information identification model of above-mentioned specify information type.
After getting sample information and sample level information, executing subject can be using above-mentioned a plurality of sample information as defeated
Enter, using sample level information corresponding with every sample information in above-mentioned a plurality of sample information as output, training is obtained pair
Answer the information identification model of above-mentioned specify information type.
Step 203, result information is constructed according to above-mentioned level information.
After obtaining level information, executing subject can construct result information by level information.Then result information
Reflect the safety of information to be processed.
In some optional implementations of the present embodiment, the above method can also include: acquisition and send on corresponding
State at least one history reference information of result information.
Usually there is the same or similar other information of result information in practice.The available result information phase of executing subject
The history reference information of same or similar other information, and history reference information is sent to user, so that user can basis
History reference information carries out relevant operation.Wherein, history reference information may include history corresponding with the above results information
Information.For example, the result information of the labour contract of Lee is identical as the result information of the labour contract of Zhang.And the labour of Zhang
Labour contract of the contract prior to Lee.The information such as the subsequent court verdict there are also Jing Guo court decision of the labour contract of Zhang.Then open
The information such as the court verdict of certain can be the corresponding history reference information of result information of Lee.
In some optional implementations of the present embodiment, the above method can also include: to believe in response to above-mentioned rank
Breath is less than setting rank, extracts from above-mentioned at least one characteristic information and shows at least one mark information.
When the level information that executing subject obtains is less than setting rank, it is believed that current information to be processed exist compared with
Big risk.At this point, executing subject can be extracted from least one characteristic information and show at least one mark information.Example
Such as, executing subject is " high-risk " to the level information obtained after a certain information processing to be processed, and sets rank as " low danger ".This
When, executing subject can extract at least one mark information from least one characteristic information of information to be processed.Wherein, on
Stating mark information is so that the level information is less than the characteristic information of setting rank.For example, being deposited at least one characteristic information
It is larger in the weighted value of some characteristic informations, the corresponding reference information of these characteristic informations.When these characteristic informations do not meet base
When the requirement of calibration information, the weighted value of reference information will directly affect the level information of information to be processed.Then executing subject can be with
Mark information is set by these characteristic informations, and shows mark information, so that user can obtain.
With continued reference to the signal that Fig. 3, Fig. 3 are according to the application scenarios of the method for obtaining information of the present embodiment
Figure.In the application scenarios of Fig. 3, information to be processed " labour contract " is sent to server 105 by terminal device 102.Server
After 105 extract characteristic information from " labour contract ", by characteristic information import information identification model, " labour contract " is corresponded to
Level information；Finally, server 105 constructs result information according to level information.
The method provided by the above embodiment of the disclosure extracts characteristic information from information to be processed first, then by feature
Information imports information identification model trained in advance, obtains the level information of information to be processed；Finally constructed according to level information
Result information.Technical scheme can obtain corresponding to the result information of information to be processed, be conducive to information to be processed
Safety judges.
With further reference to Fig. 4, it illustrates the processes 400 of another embodiment of information identification model training method.It should
The process 400 of information identification model training method, comprising the following steps:
Step 401, every galley proof in a plurality of sample information and corresponding above-mentioned a plurality of sample information of specify information type is obtained
The sample level information of this information.
In the present embodiment, executing subject (such as the clothes shown in FIG. 1 of deep learning model training method operation thereon
It is engaged in device 105) the sample rank letter of every sample information in available a plurality of sample information and corresponding above-mentioned a plurality of sample information
Breath.
In the present embodiment, the available a plurality of sample information of executing subject, and shown for those skilled in the art.Its
In, sample information may include at least one sample characteristics information.Those skilled in the art can be rule of thumb to a plurality of sample
Every sample information labeling sample level information in information.
For example, corresponding sample level information may is that prohibitive rule contrary to law when sample information is legal documents
Several ranks such as fixed, unfavorable to one's party, one's own side benefit.
Step 402, every sample information in above-mentioned a plurality of sample information is sequentially input to initial information identification model,
Obtain predicted level information corresponding to every sample information in above-mentioned a plurality of sample information.
Executing subject can sequentially input every sample information in a plurality of sample information to initial information identification model,
To obtain predicted level information corresponding to every sample information in above-mentioned a plurality of sample information.Herein, initial information is known
Other model can be the various models (such as can be deep learning model) constructed by intelligent algorithm.Executing subject can be by sample
This information is inputted from the input side of initial information identification model, successively the place of each layer parameter Jing Guo initial information identification model
Reason, and exported from the outlet side of initial information identification model, the information of outlet side output is the prediction of the corresponding sample information
Level information.Wherein, the model that initial information identification model can be unbred model or training is not completed, each layer are set
It is equipped with initiation parameter, initiation parameter can be continuously adjusted in the training process of model.
Step 403, by predicted level information corresponding to every sample information in above-mentioned a plurality of sample information and the sample
Above-mentioned sample level information corresponding to this information is compared, and obtains the predictablity rate of above-mentioned initial information identification model.
In the present embodiment, based on corresponding to every sample information in the obtained a plurality of sample information of step 402
Predicted level information, executing subject can by predicted level information corresponding to every sample information in a plurality of sample information with
Sample level information corresponding to the sample information is compared, to obtain the predictablity rate of initial information identification model.
Specifically, if predicted level information corresponding to a sample information is identical as sample level information corresponding to the sample information
Or it is close, then the prediction of initial information identification model is correct；If predicted level information corresponding to a sample information and the sample
Sample level information corresponding to information is different or not close, then initial information identification model prediction error.For example, when sample is believed
When breath is contract, corresponding sample level information may is that low danger.Corresponding predicted level information may is that high-risk.It is then initial
Information identification model prediction error；When predicted level information and sample level information are all low danger, initial information identification model
Prediction is accurate.Here, executing subject can calculate the ratio for predicting correct number and total sample number, and using the ratio as just
The predictablity rate of beginning information identification model.
Step 404, determine whether above-mentioned predictablity rate is greater than default accuracy rate threshold value.
In the present embodiment, the predictablity rate based on the obtained initial information identification model of step 403, executing subject
The predictablity rate of initial information identification model can be compared with default accuracy rate threshold value.If more than default accuracy threshold
Value, thens follow the steps 405；If thening follow the steps 406 no more than default accuracy threshold value.
For example, the predictablity rate of initial information identification model is 95% when sample information is contract, accuracy rate is preset
Threshold value is 75%, at this point it is possible to execute step 405；It, can be with when the predictablity rate of initial information identification model is lower than 75%
Execute step 406.
Step 405, the information identification model above-mentioned initial information identification model completed as training.
In the present embodiment, the case where the prediction accuracy of initial information identification model is greater than default accuracy threshold value
Under, illustrate that information identification model training is completed, at this point, executing subject can be completed initial information identification model as training
Information identification model.
Step 406, the parameter of above-mentioned initial information identification model is adjusted.
In the present embodiment, the case where the prediction accuracy of initial information identification model is not more than default accuracy threshold value
Under, the parameter of the adjustable initial information identification model of executing subject, and 402 are returned to step, it can be special until training
Until the information identification model of corresponding relationship between reference breath and level information.
In some optional implementations of the present embodiment, above-mentioned sample level information can be obtained by following steps
It arrives:
The first step extracts at least one sample characteristics information from each sample information.
In order to obtain sample level information, executing subject (such as can be server 105 shown in FIG. 1) can be from each
At least one sample characteristics information is extracted in sample information.
Second step obtains sample characteristics letter for the sample characteristics information in above-mentioned at least one sample characteristics information
Cease corresponding reference information.
After executing subject gets sample characteristics information, technical staff can be inquired corresponding to every sample characteristics information
Reference information.Wherein, said reference information is used for the rank degree of establishment numerical value to the sample characteristics information.For example, benchmark is believed
Breath can be law article etc..
Third step is corresponding sample characteristics information degree of establishment numerical value by said reference information, obtains corresponding sample
The sample level information of information.
Technical staff can be sample characteristics information degree of establishment numerical value by reference information, and then obtain corresponding sample letter
The sample level information of breath.Be conducive to judge the safety of information to be processed.For example, reference information can be law article,
When sample characteristics information meets all conditions of the law article, the sample level information for sample information setting can be " peace
Entirely "；When sample characteristics message part meets all conditions of the law article, the sample level information for sample information setting can
To be " there are risks "；When sample characteristics information is unsatisfactory for any one of all conditions of the law article, for the sample information
The sample level information of setting can be " danger ".Sample level information can also be " 100% safety ", " 90% safety ",
Forms such as " 10% safety ", depending on actual needs.
In some optional implementations of the present embodiment, said reference information may include the setting of at least one rank
Information and weighted value, and, it is corresponding sample characteristics information degree of establishment numerical value above by said reference information, can wrap
It includes: being that initial level is arranged in corresponding sample characteristics information by the rank setting information in above-mentioned at least one rank setting information
Other numerical value, and be that final rank is arranged in sample characteristics information by the weighted value of initial level numerical value and corresponding initial level numerical value
Numerical value.
The corresponding legal effect of different reference informations can be different.Executing subject can be imitated according to the law of reference information
Corresponding weighted value is arranged in power.Weighted value is bigger, then the legal effect of reference information is bigger.Reference information can also include at least
One rank setting information.Wherein, rank setting information can be the condition that reference information includes.Meet reference information simultaneously
All rank setting informations just have legal effectiveness corresponding to reference information completely.For this purpose, executing subject can pass through rank
Setting information is that initial level numerical value is arranged in corresponding sample characteristics information, and passes through initial level numerical value and corresponding initial level
The weighted value of numerical value is that final rating value is arranged in sample characteristics information.
With further reference to Fig. 5, as the realization to method shown in above-mentioned each figure, present disclose provides one kind for obtaining letter
One embodiment of the device of breath, the Installation practice is corresponding with embodiment of the method shown in Fig. 2, which can specifically answer
For in various electronic equipments.
As shown in figure 5, the present embodiment may include: feature information extraction unit for obtaining the device 500 of information
501, level information acquiring unit 502 and result information construction unit 503.Wherein, feature information extraction unit 501 is configured to
At least one characteristic information is extracted from information to be processed, wherein features described above information is for characterizing above-mentioned information to be processed
Information type；Level information acquiring unit 502 is configured to importing above-mentioned at least one characteristic information into information trained in advance
Identification model obtains the level information of above-mentioned information to be processed, wherein above- mentioned information identification model and above- mentioned information type pair
It answers, for the corresponding relationship between characteristic feature information and level information；Result information construction unit 503 is configured to according to upper
State level information building result information.
In some optional implementations of the present embodiment, above-mentioned level information acquiring unit 502 may include: information
Identification model inquires subelement (not shown), is configured to inquire the information identification model of corresponding above- mentioned information type.
In some optional implementations of the present embodiment, the above-mentioned device 500 for obtaining information can also include
Information identification model training unit (not shown) is configured to training information identification model, above- mentioned information identification model instruction
Practicing unit may include: that acquisition of information subelement (not shown) and information identification model training subelement (do not show in figure
Out).Wherein, acquisition of information subelement is configured to obtain a plurality of sample information of specify information type and corresponds to above-mentioned a plurality of sample
The sample level information of every sample information in this information, above-mentioned sample information include at least one sample characteristics information；Information
Identification model training subelement be configured to using above-mentioned a plurality of sample information as input, by in above-mentioned a plurality of sample information
For the corresponding sample level information of every sample information as output, training obtains corresponding to the information identification of above-mentioned specify information type
Model.
In some optional implementations of the present embodiment, above- mentioned information identification model training subelement may include:
Information identification model training module (not shown), be configured to by every sample information in above-mentioned a plurality of sample information according to
It is secondary to be input to initial information identification model, obtain predicted level corresponding to every sample information in above-mentioned a plurality of sample information
Information, will be corresponding to predicted level information corresponding to every sample information in above-mentioned a plurality of sample information and the sample information
Above-mentioned sample level information be compared, obtain the predictablity rate of above-mentioned initial information identification model, determine above-mentioned prediction
Whether accuracy rate is greater than default accuracy rate threshold value, if more than above-mentioned default accuracy rate threshold value, then above-mentioned initial information is identified mould
The information identification model that type is completed as training.
In some optional implementations of the present embodiment, above- mentioned information identification model training subelement can also be wrapped
Include: parameter adjustment module (not shown) is configured to adjust above-mentioned first in response to being not more than above-mentioned default accuracy rate threshold value
The parameter of beginning information identification model, and continue to execute above-mentioned training step.
In some optional implementations of the present embodiment, the above-mentioned device 500 for obtaining information can also include
Sample level information acquiring unit (not shown), is configured to obtain sample level information, and above-mentioned sample level information obtains
Take unit may include: sample characteristics information extraction subelement (not shown), reference information obtain subelement (do not show in figure
Out) and sample level information obtains subelement (not shown).Wherein, sample characteristics information extraction subelement be configured to from
At least one sample characteristics information is extracted in each sample information；Reference information obtains subelement, for above-mentioned at least one
Sample characteristics information in sample characteristics information is configured to obtain the corresponding reference information of sample characteristics information, above-mentioned base
Calibration information is used for the rank degree of establishment numerical value to the sample characteristics information；Sample level information obtains subelement and is configured to lead to
Crossing said reference information is corresponding sample characteristics information degree of establishment numerical value, obtains the sample rank letter of corresponding sample information
Breath.
In some optional implementations of the present embodiment, said reference information may include the setting of at least one rank
Information and weighted value, and, it includes: that sample level information obtains module (in figure not that above-mentioned sample level information, which obtains subelement,
Show), it is configured to through the rank setting information in above-mentioned at least one rank setting information be corresponding sample characteristics letter
Breath setting initial level numerical value, and be sample characteristics information by the weighted value of initial level numerical value and corresponding initial level numerical value
Final rating value is set.
In some optional implementations of the present embodiment, the above-mentioned device 500 for obtaining information can also include:
History reference information transmitting unit (not shown) is configured to obtain and send at least one of corresponding the above results information
History reference information, wherein above-mentioned history reference information includes historical information corresponding with the above results information.
In some optional implementations of the present embodiment, above-mentioned apparatus further include: prompt unit, in response to above-mentioned grade
Other information is less than setting rank, is configured to extract and show from above-mentioned at least one characteristic information that at least one label is believed
Breath, above-mentioned mark information are so that above-mentioned level information is less than the characteristic information of setting rank.
In some optional implementations of the present embodiment, above- mentioned information type includes at least one of the following: law text
Book, security information.
The present embodiment additionally provides a kind of electronic equipment, comprising: one or more processors；Memory is stored thereon with
One or more programs, when said one or multiple programs are executed by said one or multiple processors, so that said one
Or multiple processors execute the above-mentioned method for obtaining information.
The present embodiment additionally provides a kind of computer-readable medium, is stored thereon with computer program, and the program is processed
Device realizes the above-mentioned method for obtaining information when executing.
Below with reference to Fig. 6, it illustrates the electronic equipments for being suitable for being used to realize embodiment of the disclosure (for example, in Fig. 1
Terminal device 101,102,103 and/or server 105) computer system 600 structural schematic diagram.Electronics shown in Fig. 6 is set
A standby only example, should not function to embodiment of the disclosure and use scope bring any restrictions.
As shown in fig. 6, electronic equipment 600 may include processing unit (such as central processing unit, graphics processor etc.)
601, random access can be loaded into according to the program being stored in read-only memory (ROM) 602 or from storage device 608
Program in memory (RAM) 603 and execute various movements appropriate and processing.In RAM 603, it is also stored with electronic equipment
Various programs and data needed for 600 operations.Processing unit 601, ROM 602 and RAM 603 pass through the phase each other of bus 604
Even.Input/output (I/O) interface 605 is also connected to bus 604.
In general, following device can connect to I/O interface 605: including such as touch screen, touch tablet, keyboard, mouse, taking the photograph
As the input unit 606 of head, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaker, vibration
The output device 607 of dynamic device etc.；Storage device 608 including such as tape, hard disk etc.；And communication device 609.Communication device
609, which can permit electronic equipment 600, is wirelessly or non-wirelessly communicated with other equipment to exchange data.Although Fig. 6 shows tool
There is the electronic equipment 600 of various devices, it should be understood that being not required for implementing or having all devices shown.It can be with
Alternatively implement or have more or fewer devices.Each box shown in Fig. 6 can represent a device, can also root
According to needing to represent multiple devices.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communication device 609, or from storage device 608
It is mounted, or is mounted from ROM 602.When the computer program is executed by processing unit 601, the implementation of the disclosure is executed
The above-mentioned function of being limited in the method for example.
It is situated between it should be noted that the above-mentioned computer-readable medium of embodiment of the disclosure can be computer-readable signal
Matter or computer readable storage medium either the two any combination.Computer readable storage medium for example can be with
System, device or the device of --- but being not limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or it is any more than
Combination.The more specific example of computer readable storage medium can include but is not limited to: have one or more conducting wires
Electrical connection, portable computer diskette, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type are programmable
Read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic are deposited
Memory device or above-mentioned any appropriate combination.In embodiment of the disclosure, computer readable storage medium, which can be, appoints
What include or the tangible medium of storage program that the program can be commanded execution system, device or device use or and its
It is used in combination.And in embodiment of the disclosure, computer-readable signal media may include in a base band or as carrier wave
The data-signal that a part is propagated, wherein carrying computer-readable program code.The data-signal of this propagation can be adopted
With diversified forms, including but not limited to electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal is situated between
Matter can also be any computer-readable medium other than computer readable storage medium, which can be with
It sends, propagate or transmits for by the use of instruction execution system, device or device or program in connection.Meter
The program code for including on calculation machine readable medium can transmit with any suitable medium, including but not limited to: electric wire, optical cable,
RF (radio frequency) etc. or above-mentioned any appropriate combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not
It is fitted into the electronic equipment.Above-mentioned computer-readable medium carries one or more program, when said one or more
When a program is executed by the electronic equipment, so that the electronic equipment: at least one characteristic information is extracted from information to be processed,
In, characteristic information is used to characterize the information type of above-mentioned information to be processed；Above-mentioned at least one characteristic information is imported into instruction in advance
Experienced information identification model obtains the level information of above-mentioned information to be processed, above- mentioned information identification model and above- mentioned information type
It is corresponding, for the corresponding relationship between characteristic feature information and level information；Result information is constructed according to above-mentioned level information.
The behaviour for executing embodiment of the disclosure can be write with one or more programming languages or combinations thereof
The computer program code of work, above procedure design language include object oriented program language-such as Java,
Smalltalk, C++ further include conventional procedural programming language-such as " C " language or similar program design language
Speech.Program code can be executed fully on the user computer, partly be executed on the user computer, as an independence
Software package execute, part on the user computer part execute on the remote computer or completely in remote computer or
It is executed on server.In situations involving remote computers, remote computer can pass through the network of any kind --- packet
It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit
It is connected with ISP by internet).
Flow chart and block diagram in attached drawing illustrate system, method and the computer of the various embodiments according to the disclosure
The architecture, function and operation in the cards of program product.In this regard, each box in flowchart or block diagram can be with
A part of a module, program segment or code is represented, a part of the module, program segment or code includes one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, Ke Yiyong
The dedicated hardware based system of defined functions or operations is executed to realize, or can be referred to specialized hardware and computer
The combination of order is realized.
Being described in unit involved in embodiment of the disclosure can be realized by way of software, can also be passed through
The mode of hardware is realized.Described unit also can be set in the processor, for example, can be described as: a kind of processor
Including feature information extraction unit, level information acquiring unit and result information construction unit.Wherein, the title of these units exists
The restriction to the unit itself is not constituted in the case of certain, for example, result information construction unit is also described as " obtaining
The unit of result information ".
Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (22)
1. a kind of method for obtaining information, comprising:
At least one characteristic information is extracted from information to be processed, wherein the characteristic information is for characterizing the letter to be processed
The information type of breath；
At least one characteristic information is imported to information identification model trained in advance, obtains the rank of the information to be processed
Information；
Wherein, the information identification model is corresponding with the information type, between characteristic feature information and level information
Corresponding relationship；And
Result information is constructed according to the level information.
2. described that at least one characteristic information is imported to letter trained in advance according to the method described in claim 1, wherein
Identification model is ceased, the level information of the information to be processed is obtained, comprising:
The information identification model of the corresponding information type of inquiry.
3. according to the method described in claim 1, wherein, the information identification model is obtained by following steps training:
Obtain the sample of every sample information in a plurality of sample information and the corresponding a plurality of sample information of specify information type
Level information, the sample information include at least one sample characteristics information；
Using a plurality of sample information as input, by sample corresponding with every sample information in a plurality of sample information
Level information obtains corresponding to the information identification model of the specify information type as output, training.
It is described using a plurality of sample information as input 4. according to the method described in claim 3, wherein, will with it is described more
As output, training obtains corresponding to the specify information the corresponding sample level information of every sample information in sample information
The information identification model of type, comprising:
It executes following training step: every sample information in a plurality of sample information being sequentially input to initial information and is identified
Model obtains predicted level information corresponding to every sample information in a plurality of sample information, by a plurality of sample
The sample level information corresponding to predicted level information corresponding to every sample information in information and the sample information
It is compared, obtains the predictablity rate of the initial information identification model, it is default to determine whether the predictablity rate is greater than
Accuracy rate threshold value, if more than the default accuracy rate threshold value, then using the initial information identification model as the letter of training completion
Cease identification model.
It is described using a plurality of sample information as input 5. according to the method described in claim 4, wherein, will with it is described more
As output, training obtains corresponding to the specify information the corresponding sample level information of every sample information in sample information
The information identification model of type, further includes:
In response to being not more than the default accuracy rate threshold value, the parameter of the initial information identification model is adjusted, and continue to execute
The training step.
6. according to the method described in claim 3, wherein, the sample level information is obtained by following steps:
At least one sample characteristics information is extracted from each sample information；
For the sample characteristics information in at least one sample characteristics information, the corresponding benchmark of sample characteristics information is obtained
Information, the reference information are used for the rank degree of establishment numerical value to the sample characteristics information；
It is corresponding sample characteristics information degree of establishment numerical value by the reference information, obtains the sample grade of corresponding sample information
Other information.
7. according to the method described in claim 6, wherein, the reference information includes at least one rank setting information and weight
Value, and
It is described by the reference information be corresponding sample characteristics information degree of establishment numerical value, comprising:
It is that the setting of corresponding sample characteristics information is initial by the rank setting information in at least one rank setting information
Rating value, and be that final level is arranged in sample characteristics information by the weighted value of initial level numerical value and corresponding initial level numerical value
Other numerical value.
8. according to the method described in claim 1, wherein, the method also includes:
Obtain and send at least one history reference information of the corresponding result information, wherein the history reference packet
Include historical information corresponding with the result information.
9. according to the method described in claim 1, wherein, the method also includes:
It is less than setting rank in response to the level information, is extracted from least one characteristic information and show at least one
Mark information, the mark information are so that the level information is less than the characteristic information of setting rank.
10. according to claim 1 to method described in 9 any one, wherein the information type includes at least one of the following:
Legal documents, security information.
11. a kind of for obtaining the device of information, comprising:
Feature information extraction unit is configured to extract at least one characteristic information from information to be processed, wherein the feature
Information is used to characterize the information type of the information to be processed；
Level information acquiring unit is configured to import at least one characteristic information information identification mould trained in advance
Type obtains the level information of the information to be processed, wherein the information identification model is corresponding with the information type, is used for
Corresponding relationship between characteristic feature information and level information；
Result information construction unit is configured to construct result information according to the level information.
12. the apparatus according to claim 1, wherein the level information acquiring unit includes:
Information identification model inquires subelement, is configured to inquire the information identification model of the corresponding information type.
13. the apparatus according to claim 1, wherein described device further includes information identification model training unit, is configured
At training information identification model, the information identification model training unit includes:
Acquisition of information subelement, a plurality of sample information and the corresponding a plurality of sample for being configured to obtain specify information type are believed
The sample level information of every sample information in breath, the sample information include at least one sample characteristics information；
Information identification model trains subelement, is configured to using a plurality of sample information as input, will be with a plurality of sample
As output, training obtains corresponding to the specify information type the corresponding sample level information of every sample information in this information
Information identification model.
14. device according to claim 13, wherein information identification model training subelement includes:
Information identification model training module, be configured to sequentially input every sample information in a plurality of sample information to
Initial information identification model obtains predicted level information corresponding to every sample information in a plurality of sample information, will
Predicted level information corresponding to every sample information in a plurality of sample information with it is described corresponding to the sample information
Sample level information is compared, and is obtained the predictablity rate of the initial information identification model, is determined the predictablity rate
Whether be greater than default accuracy rate threshold value, if more than the default accuracy rate threshold value, then using the initial information identification model as
The information identification model that training is completed.
15. device according to claim 14, wherein the information identification model training subelement further include:
Parameter adjustment module is configured to adjust the initial information identification in response to being not more than the default accuracy rate threshold value
The parameter of model, and continue to execute the training step.
16. device according to claim 13, wherein described device further includes sample level information acquiring unit, is matched
It is set to and obtains sample level information, the sample level information acquiring unit includes:
Sample characteristics information extraction subelement is configured to extract at least one sample characteristics letter from each sample information
Breath；
Reference information obtains subelement and the sample characteristics information in at least one sample characteristics information is configured to
The corresponding reference information of sample characteristics information is obtained, the reference information is used to that grade to be arranged to the rank of the sample characteristics information
Other numerical value；
Sample level information obtains subelement, is configured to be that grade is arranged in corresponding sample characteristics information by the reference information
Other numerical value obtains the sample level information of corresponding sample information.
17. device according to claim 16, wherein the reference information includes at least one rank setting information and power
Weight values, and
The sample level information obtains subelement
Sample level information obtains module, is configured to through the rank setting information in at least one rank setting information
Initial level numerical value is set for corresponding sample characteristics information, and passes through the power of initial level numerical value and corresponding initial level numerical value
Weight values are that final rating value is arranged in sample characteristics information.
18. device according to claim 11, wherein described device further include:
History reference information transmitting unit is configured to obtain and send at least one history reference of the corresponding result information
Information, wherein the history reference information includes historical information corresponding with the result information.
19. device according to claim 11, wherein described device further include:
Prompt unit is less than setting rank in response to the level information, is configured to from least one characteristic information
Extract and show that at least one mark information, the mark information are so that the level information is less than setting grade another characteristic letter
Breath.
20. device described in 1 to 19 any one according to claim 1, wherein the information type includes following at least one
: legal documents, security information.
21. a kind of electronic equipment, comprising:
One or more processors；
Memory is stored thereon with one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors
Perform claim requires any method in 1 to 10.
22. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that the program is executed by processor
Method of the Shi Shixian as described in any in claims 1 to 10.
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