CN110019706A - A kind of problem generation method and device - Google Patents
A kind of problem generation method and device Download PDFInfo
- Publication number
- CN110019706A CN110019706A CN201711092223.5A CN201711092223A CN110019706A CN 110019706 A CN110019706 A CN 110019706A CN 201711092223 A CN201711092223 A CN 201711092223A CN 110019706 A CN110019706 A CN 110019706A
- Authority
- CN
- China
- Prior art keywords
- knowledge point
- answer
- knowledge
- historical
- knoeledge
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Human Computer Interaction (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the present application discloses a kind of problem generation method and device, pass through the historical problem of acquisition, feature extraction rule corresponding with the enquirement type can be determined according to the enquirement type of the historical problem, knowledge point can be extracted from the answer of the historical problem according to this feature extracting rule, and new problem is generated with this knowledge point, thus the intersystem problem that expands knowledge quantity, so that the problem of saved in knowledge system is no longer that can only put question to obtain according to user, to extend the mode of completely new extended problem, effectively increase the problems in knowledge system ownership.And the knowledge point by being extracted is related to the knowledge content that user may wish to understanding, therefore the problem of being generated according to knowledge point, is likely to be user because wanting to know about the problem of knowledge point is proposed, to when user proposes the problem, knowledge system can provide the answer of the problem quickly, reduce user to a certain extent and put question to the case where other users can only being waited to answer appearance.
Description
Technical field
This application involves data processing fields, more particularly to a kind of problem generation method and device.
Background technique
Save a large amount of corresponding answers of problem and problem in the knowledge system of interactive, user can be by knowledge
System put question to and get help, if such as user enquirement and knowledge system in save the problem of same or similar, knowledge system
Answer corresponding to the problem saved can be supplied to user
But once the enquirement of user is different from the problem of preservation in knowledge system, knowledge system will push away the enquirement
Other users answer is given, during other users provide answer, the user of enquirement can only be waited.
If intersystem problem and answer can effectively be expanded knowledge, can be improved match user asked a question it is several
There is the case where user waits answer to reduce in rate.However, by it is saved in knowledge system the problem of typically history
The problem of user is putd question in data, thus in knowledge system the limited amount of saved problem and with whether have user's enquirement
Correlation is crossed, therefore there has been no the mechanism of problem and answer in the system that effectively expands knowledge at present.
Summary of the invention
In order to solve the above-mentioned technical problem, it this application provides a kind of problem generation method and device, extends completely new
The mode of extended problem, effectively increases the problems in knowledge system ownership, and reducing user's enquirement to a certain extent can only
The case where waiting other users to answer occurs, and improves the experience that user uses knowledge system.
The embodiment of the present application discloses following technical solution:
In a first aspect, the embodiment of the present application provides a kind of problem generation method, which comprises
Obtain historical problem；
Feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem；
Knowledge point is extracted from the answer of the historical problem according to the feature extraction rule；
Problem is generated according to the knowledge point.
Optionally, described that knowledge point is extracted from the answer of the historical problem according to the feature extraction rule, packet
It includes:
Meta-knoeledge is extracted from the answer of the historical problem according to the feature extraction rule, the meta-knoeledge is used for
Embody the knowledge content in the answer of the historical problem with the Knowledge Relation；
The knowledge point is determined according to the answer of the meta-knoeledge and the historical problem.
It is optionally, described that problem is generated according to the knowledge point, comprising:
According to the incidence relation of the meta-knoeledge and the knowledge point, corresponding enquirement mode is selected；
Described problem is generated according to selected enquirement mode and the knowledge point.
Optionally, after described according to the knowledge point generation problem, further includes:
It is that the described problem generated determines corresponding answer according to the meta-knoeledge.
Optionally, described that knowledge point is extracted from the answer of the historical problem according to the feature extraction rule, packet
It includes:
Knowledge is extracted from the answer of the historical problem in conjunction with the historical problem according to the feature extraction rule
Point.
It is optionally, described that problem is generated according to the knowledge point, comprising:
Problem is generated according to preset enquirement mode and the knowledge point.
Second aspect, the embodiment of the present application provide a kind of problem generating means, and described device includes acquiring unit, determination
Unit, extraction unit and generation unit:
The acquiring unit, for obtaining historical problem；
The determination unit, for determining spy corresponding with the enquirement type according to the enquirement type of the historical problem
Levy extracting rule；
The extraction unit, for extracting knowledge from the answer of the historical problem according to the feature extraction rule
Point；
The generation unit, for generating problem according to the knowledge point.
Optionally, the extraction unit includes: that meta-knoeledge extraction subelement and knowledge point determine subelement；
The meta-knoeledge extracts subelement, for being mentioned from the answer of the historical problem according to the feature extraction rule
Meta-knoeledge is taken out, the meta-knoeledge is used to embody the knowledge content in the answer of the historical problem with the Knowledge Relation；
The knowledge point determines subelement, described for being determined according to the answer of the meta-knoeledge and the historical problem
Knowledge point.
Optionally, the generation unit includes: selection subelement and generation subelement；
The selection subelement selects corresponding mention for the incidence relation according to the meta-knoeledge and the knowledge point
Ask mode；
The generation subelement, for generating described problem according to selected enquirement mode and the knowledge point.
Optionally, described device further includes answer determination unit, for being the described problem generated according to the meta-knoeledge
Determine corresponding answer.
Optionally, the extraction unit includes:
Subelement is extracted in knowledge point, for being asked in conjunction with the historical problem from the history according to the feature extraction rule
Knowledge point is extracted in the answer of topic.
Optionally, the generation unit is also used to generate problem according to preset enquirement mode and the knowledge point.
The third aspect, the embodiment of the present application provide a kind of processing equipment generated for problem, include memory, with
And one perhaps more than one program one of them or more than one program be stored in memory, and be configured to by
It includes the instruction for performing the following operation that one or more than one processor, which execute the one or more programs:
Obtain historical problem；
Feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem；
Knowledge point is extracted from the answer of the historical problem according to the feature extraction rule；
Problem is generated according to the knowledge point.
Fourth aspect, the embodiment of the present application provide a kind of machine readable media, are stored thereon with instruction, when by one or
When multiple processors execute, so that device is executed such as the problems in first aspect generation method.
It, can be according to the enquirement class of the historical problem by the historical problem obtained it can be seen from above-mentioned technical proposal
Type determines feature extraction rule corresponding with the enquirement type, according to this feature extracting rule from the answer of the historical problem
Knowledge point can be extracted, and generates new problem with this knowledge point, the intersystem problem that thus expands knowledge quantity makes to learn
The problem of saved in knowledge system is no longer that can only put question to obtain according to user, to extend the side of completely new extended problem
Formula effectively increases the problems in knowledge system ownership.Moreover, because the knowledge point extracted and user may wish to
The knowledge content of solution is related, therefore the problem of being generated according to knowledge point is likely to be user is proposed because wanting to know about the knowledge point
The problem of, so that knowledge system can provide the answer of the problem quickly when user proposes the problem, subtract to a certain extent
Lack user and putd question to the case where other users can only being waited to answer appearance, improves the experience that user uses knowledge system.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of application without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is a kind of method flow diagram of problem generation method provided by the embodiments of the present application；
Fig. 2 is a kind of structure drawing of device of problem generating means provided by the embodiments of the present application；
Fig. 3 is a kind of block diagram of device generated for problem provided by the embodiments of the present application；
Fig. 4 is a kind of block diagram of server generated for problem provided by the embodiments of the present application.
Specific embodiment
With reference to the accompanying drawing, embodiments herein is described.
Save a large amount of corresponding answers of problem and problem in the knowledge system of interactive, user can be by knowledge
System is putd question to and is got help.But what is stored in knowledge system is all that the problem of first user is mentioned and the problem are corresponding
Reply, if proposing the problem of active user is mentioned without first user, knowledge system will be unable to get active user institute
The problem can only be pushed and be given to other platforms or other users to get and to answer by the answer asked a question.Actually answering
In, the problem of being pushed to other platforms or other users, get that answer the needed to wait for time generally all long, thus
Not high experience is brought to user.
If intersystem problem storage can be expanded knowledge as far as possible, it a degree of can reduce the user putd question to and wait
The case where other users are answered a question.But in traditional knowledge system building, it is saved the problem of be mainly to be fixed against history
Problem data needs once to have the problem of user putd question to, be possible to extend in knowledge system, knowledge system does not have
The problem of having the ability for createing new problem without foundation, therefore possessing in knowledge system quantity be it is more limited, that uniquely expands can
It can only expect have user to propose new problem.As it can be seen that there has been no problem in the system that effectively expands knowledge and answers at present
Mechanism can not depend on user and put question to come the intersystem problem reserves that expand knowledge.
For this purpose, the embodiment of the present application provides a kind of problem generation method, it, can be according to this by the historical problem of acquisition
The enquirement type of historical problem determines corresponding with enquirement type feature extraction rule, according to this feature extracting rule from this
Knowledge point is extracted in the answer of historical problem, and new problem is generated with this knowledge point, asking in the system that thus expands knowledge
Inscribe quantity so that it is saved in knowledge system the problem of be no longer that can only put question to obtain according to user, to extend completely new
The mode of extended problem effectively increases the problems in knowledge system ownership.Moreover, because the knowledge point extracted and use
It is related that family may wish to the knowledge content understood, therefore the problem of being generated according to knowledge point is likely to be user because wanting to know about this
The problem of knowledge point is proposed, so that knowledge system can provide the answer of the problem quickly when user proposes the problem,
Reduce user to a certain extent and put question to the case where other users can only being waited to answer appearance, improves user and use knowledge system
Experience.
Fig. 1 is a kind of method flow diagram of problem generation method provided by the embodiments of the present application, this method comprises:
S101: historical problem is obtained.
The historical problem can be historical user it has been suggested that the problem of, be also possible in internet for common sense or basis
The problem of knowledge formation.The historical problem can have been saved in the knowledge system for needing extended problem.Due to being to have mentioned
Historical problem out, therefore the historical problem should have corresponding answer.Such as a historical problem can be that " which Great Wall has have
The legend of the meaning? ", corresponding answer can be " Meng Jiangnv cry Great Wall ", " goat carries brick on the back ", " ice tunnel stone transporting " etc..
S102: feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem.
There are different clause, semantic meaning representation mode etc. due to puing question to, so as to determine the enquirement of historical problem with this
Type, for example, may include be non-class, enumerate class, validation of information class etc..Different enquirement types can correspond to different features
The feature extraction rule of extracting rule, an enquirement type refers to from the answer for having the problem of this puts question to type corresponding
Extract the rule of feature.The feature extraction rule may include the mode extracted, feature to be extracted in problem to be extracted
Position, such as feature to be extracted belong to problem to be extracted and correspond to some component part of sentence etc..According to an enquirement type institute
Corresponding feature extraction rule effectively can extract required content from the answer for puing question to type problem with this.
S103: knowledge point is extracted from the answer of the historical problem according to this feature extracting rule.
Explanation such as S102 to feature extraction rule, can be according to this feature extracting rule effectively from the historical problem
Knowledge point is extracted in answer.The knowledge point can be include association knowledge content feature, which can be with
Embodying user may understanding demand to the knowledge point.
The knowledge point can be directly included in the answer of the historical problem, can also in the answer of the historical problem
Content is related.The association knowledge content can appear in the answer of the historical problem, can also not appear in the historical problem
Answer in.
Such as historical problem be " stripping taro after feel hand fiber crops itch it is hard to bear what if? ", corresponding answer is " to be contained with washbowl
A little vinegar is added in a little clear water, is used to wash one's hands after mixing evenly, can also be antipruritic ".The enquirement type of the historical problem is to enumerate
Class can extract knowledge point " vinegar " according to the corresponding feature extraction rule of class is enumerated from the answer." vinegar " this know
Knowing the included association knowledge content of point may include " vinegar particularly acts on the fiber crops that can prevent hand stripping taro from generating and itches ", should
Association knowledge content, which embodies user, may want to understand the demand of the particular utility of vinegar.Certainly, " vinegar " this knowledge point
Included association knowledge content can also have other, for example, it is relevant to diet, with go stain relevant etc..
Other than it can extract knowledge point from the answer of historical problem according to feature extraction rule, mentioned from answer
When taking knowledge point, the extraction that the corresponding historical problem of the answer carries out knowledge point from the answer can be combined with.Pass through analysis
Historical problem can meaning that more different piece may embody clearly in the answer of the historical problem, so as to further mention
Height extracts the accuracy of knowledge point from answer.
S104: problem is generated according to knowledge point.
The knowledge point as acquired in S103 belongs to user may know about the feature of demand to it, therefore according to the knowledge point
The problem of generation, will be user and be possible to because of the problem of demand wanted to know about is proposed.
The embodiment of the present application does not limit how according to knowledge point generation problem, as long as the enquirement in spoken and written languages habit
Sentence.Such as knowledge point " goat carries brick on the back " is determined according to historical problem and answer, can be with this problem generated
" goat carries the story what brick is on the back? ".
The similitude of problem may be proposed in order to improve problem generated and user, it can be according to preset enquirement mode
Problem is generated with the knowledge point, which can refine to obtain according to the proposed problem of user in historical data,
The clause that can usually put question to user, word are similar, are matched to the probability that user is asked a question to improve.
For example, determined knowledge point " vinegar " according to historical problem and answer, preset enquirement mode may include " ... be
What? ", " ... it is there what effect? " Deng, then the problem of being generated according to knowledge point can for " what vinegar is? ", " vinegar has
What effect? " Deng.
As it can be seen that can be determined and the enquirement class according to the enquirement type of the historical problem by the historical problem obtained
Type corresponding feature extraction rule extracts knowledge point according to this feature extracting rule from the answer of the historical problem, and with
This knowledge point generates new problem, the intersystem problem that thus expands knowledge quantity so that it is saved in knowledge system the problem of
No longer it is that can only put question to obtain according to user, to extend the mode of completely new extended problem, effectively increases knowledge system
The problems in ownership.Moreover, because the knowledge point extracted is related to the knowledge content that user may wish to understanding, therefore root
The problem of generating according to knowledge point is likely to be user because wanting to know about the problem of knowledge point is proposed, thus when user proposes
When the problem, knowledge system can provide the answer of the problem quickly, reduce user to a certain extent and put question to and can only wait
The case where other users are answered occurs, and improves the experience that user uses knowledge system.
Next a kind of optional embodiment for S103 will be further illustrated.
When implementing S103, meta-knoeledge first can be extracted from the answer of historical problem according to feature extraction rule, then
Knowledge point is determined according to the answer of meta-knoeledge and historical problem.
Meta-knoeledge described here is used to embody the knowledge content in the answer of historical problem with Knowledge Relation.By this
Knowledge content can allow user to understand information relevant to the knowledge point.
By first extracting meta-knoeledge, the range of knowledge point needed for can effectively defining, the model determined by the meta-knoeledge
A possibility that knowledge content that there is user to want to know about for the knowledge point extracted in enclosing, is more preferable, improves the available of knowledge point
Property.
For example, historical problem be " stripping taro after feel hand fiber crops itch it is hard to bear what if? ", answer is " to be contained with washbowl a little clear
A little vinegar is added in water, is used to wash one's hands after mixing evenly, can also be antipruritic ".The meta-knoeledge determined from the answer can be
" stripping taro ", " hand fiber crops itch hard to bear ", the knowledge point determined should be related to above-mentioned meta-knoeledge.It is asked according to above-mentioned meta-knoeledge and history
The answer of topic can determine knowledge point " vinegar " relevant to above-mentioned meta-knoeledge.
The knowledge point determined according to aforesaid way, can generate problem according to mode described in S104, such as can be with
Problem is generated according to preset enquirement mode and the knowledge point.In addition to this, for S104, the embodiment of the present application also provides one
The mode of kind generation problem.
Specifically: after determining knowledge point according to meta-knoeledge, it can determine the incidence relation of meta-knoeledge and knowledge point,
Corresponding enquirement mode is selected further according to the incidence relation determined, is generated and is asked according to selected enquirement mode and knowledge point
Topic.
If the incidence relation obtained in the clear S103 of energy between knowledge point and corresponding meta-knoeledge, can also close according to the association
System, selection can embody the enquirement mode of this incidence relation.If the knowledge point is generated problem by the enquirement mode, then
It may include the meta-knoeledge in answer corresponding to the problem, or obtained according to meta-knoeledge determination.To eliminate knowledge
Subsequent time and the data handling procedure that answer is determined for institute's generation problem of system, improves treatment effeciency.
That is, further can also determine answer after S104 for S104 problem generated.In addition to above-mentioned
It can be obtained according to meta-knoeledge according to the incidence relation selection enquirement mode problem generated between knowledge point and corresponding meta-knoeledge
Outside corresponding answer, the problem of can also generating other situations, is not easy in other words in the case where directly determining out answer, can be with
User will be pushed to the problem of generation in advance to answer or crawl by network, later answering using the answer of verification as the problem
Case can directly transfer corresponding answer and be supplied to user thus when there is user to propose the enquirement similar with the problem.
For example, determining that meta-knoeledge is that " vinegar, which particularly acts on, can prevent hand from shelling taro according to historical problem and answer
The fiber crops of generation itch ", corresponding knowledge point is " vinegar ", and " vinegar particularly acts on the fiber crops that can prevent hand stripping taro from generating and itches "
A kind of specific use of " vinegar " is embodied, therefore corresponding enquirement mode can be selected according to incidence relation " specific use ",
It can choose the enquirement mode enumerated class and unconventional purposes can be embodied, finally according to the selected new problem for puing question to schema creation
Can be " what unexpected use vinegar has? ".
Moreover, because the mode of enquirement is selected according to the incidence relation between knowledge point and meta-knoeledge, therefore in addition to that can give birth to
It is problematic outer, can also be determined according to the meta-knoeledge the corresponding answer of generation problem be " if both hands are sent out because scraping taro
Itch, a little vinegar can be added in clear water, can be antipruritic after washing ".Thus when user uses knowledge system, if to vinegar
When particular utility is interested, it is proposed the problem of " it is unexpected what vinegar has the problem of there is a strong possibility with above-mentioned generation
Use? " match, then knowledge system can by predetermined answer " if both hands are itched because scraping taro, can be clear
, can be antipruritic after washing in water plus a little vinegar " it is pushed to the user, a possibility which meets user demand, is higher, from
And improve the usage experience of user.
Fig. 2 is a kind of structure drawing of device of problem generating means provided by the embodiments of the present application, and described device includes obtaining
Unit 201, determination unit 202, extraction unit 203 and generation unit 204:
The acquiring unit 201, for obtaining historical problem；
The determination unit 202, for corresponding with the enquirement type according to the determination of the enquirement type of the historical problem
Feature extraction rule；
The extraction unit 203, for being extracted from the answer of the historical problem according to the feature extraction rule
Knowledge point；
The generation unit 204, for generating problem according to the knowledge point.
Optionally, the extraction unit includes: that meta-knoeledge extraction subelement and knowledge point determine subelement；
The meta-knoeledge extracts subelement, for being mentioned from the answer of the historical problem according to the feature extraction rule
Meta-knoeledge is taken out, the meta-knoeledge is used to embody the knowledge content in the answer of the historical problem with the Knowledge Relation；
The knowledge point determines subelement, described for being determined according to the answer of the meta-knoeledge and the historical problem
Knowledge point.
Optionally, the generation unit includes: selection subelement and generation subelement；
The selection subelement selects corresponding mention for the incidence relation according to the meta-knoeledge and the knowledge point
Ask mode；
The generation subelement, for generating described problem according to selected enquirement mode and the knowledge point.
Optionally, described device further includes answer determination unit, for being the described problem generated according to the meta-knoeledge
Determine corresponding answer.
Optionally, the extraction unit includes that subelement is extracted in knowledge point, for being combined according to the feature extraction rule
The historical problem extracts knowledge point from the answer of the historical problem.
Optionally, the generation unit is also used to generate problem according to preset enquirement mode and the knowledge point.
As it can be seen that can be determined and the enquirement class according to the enquirement type of the historical problem by the historical problem obtained
Type corresponding feature extraction rule extracts knowledge point according to this feature extracting rule from the answer of the historical problem, and with
This knowledge point generates new problem, the intersystem problem that thus expands knowledge quantity so that it is saved in knowledge system the problem of
No longer it is that can only put question to obtain according to user, to extend the mode of completely new extended problem, effectively increases knowledge system
The problems in ownership.Moreover, because the knowledge point extracted is related to the knowledge content that user may wish to understanding, therefore root
The problem of generating according to knowledge point is likely to be user because wanting to know about the problem of knowledge point is proposed, thus when user proposes
When the problem, knowledge system can provide the answer of the problem quickly, reduce user to a certain extent and put question to and can only wait
The case where other users are answered occurs, and improves the experience that user uses knowledge system.
Fig. 3 is a kind of block diagram of device 300 for speech synthesis shown according to an exemplary embodiment.For example, dress
Setting 300 can be robot, mobile phone, computer, digital broadcasting terminal, messaging device, game console, and plate is set
It is standby, Medical Devices, body-building equipment, personal digital assistant etc..
Referring to Fig. 3, device 300 may include following one or more components: processing component 302, memory 304, power supply
Component 306, multimedia component 308, audio component 310, the interface 312 of input/output (I/O), sensor module 314, and
Communication component 316.
The integrated operation of the usual control device 300 of processing component 302, such as with display, telephone call, data communication, phase
Machine operation and record operate associated operation.Processing element 302 may include that one or more processors 320 refer to execute
It enables, to perform all or part of the steps of the methods described above.In addition, processing component 302 may include one or more modules, just
Interaction between processing component 302 and other assemblies.For example, processing component 302 may include multi-media module, it is more to facilitate
Interaction between media component 303 and processing component 302.
Memory 304 is configured as storing various types of data to support the operation in device 300.These data are shown
Example includes the instruction of any application or method for operating on the device 300, contact data, and telephone book data disappears
Breath, picture, video etc..Memory 304 can be by any kind of volatibility or non-volatile memory device or their group
It closes and realizes, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM) is erasable to compile
Journey read-only memory (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic memory, flash
Device, disk or CD.
Power supply module 306 provides electric power for the various assemblies of device 300.Power supply module 306 may include power management system
System, one or more power supplys and other with for device 300 generate, manage, and distribute the associated component of electric power.
Multimedia component 308 includes the screen of one output interface of offer between described device 300 and user.One
In a little embodiments, screen may include liquid crystal display (LCD) and touch panel (TP).If screen includes touch panel, screen
Curtain may be implemented as touch screen, to receive input signal from the user.Touch panel includes one or more touch sensings
Device is to sense the gesture on touch, slide, and touch panel.The touch sensor can not only sense touch or sliding action
Boundary, but also detect duration and pressure associated with the touch or slide operation.In some embodiments, more matchmakers
Body component 308 includes a front camera and/or rear camera.When device 300 is in operation mode, such as screening-mode or
When video mode, front camera and/or rear camera can receive external multi-medium data.Each front camera and
Rear camera can be a fixed optical lens system or have focusing and optical zoom capabilities.
Audio component 310 is configured as output and/or input audio signal.For example, audio component 310 includes a Mike
Wind (MIC), when device 300 is in operation mode, when such as call mode, recording mode, and voice recognition mode, microphone is matched
It is set to reception external audio signal.The received audio signal can be further stored in memory 304 or via communication set
Part 316 is sent.In some embodiments, audio component 310 further includes a loudspeaker, is used for output audio signal.
I/O interface 312 provides interface between processing component 302 and peripheral interface module, and above-mentioned peripheral interface module can
To be keyboard, click wheel, button etc..These buttons may include, but are not limited to: home button, volume button, start button and lock
Determine button.
Sensor module 314 includes one or more sensors, and the state for providing various aspects for device 300 is commented
Estimate.For example, sensor module 314 can detecte the state that opens/closes of device 300, and the relative positioning of component, for example, it is described
Component is the display and keypad of device 300, and sensor module 314 can be with 300 1 components of detection device 300 or device
Position change, the existence or non-existence that user contacts with device 300,300 orientation of device or acceleration/deceleration and device 300
Temperature change.Sensor module 314 may include proximity sensor, be configured to detect without any physical contact
Presence of nearby objects.Sensor module 314 can also include optical sensor, such as CMOS or ccd image sensor, at
As being used in application.In some embodiments, which can also include acceleration transducer, gyro sensors
Device, Magnetic Sensor, pressure sensor or temperature sensor.
Communication component 316 is configured to facilitate the communication of wired or wireless way between device 300 and other equipment.Device
300 can access the wireless network based on communication standard, such as WiFi, 2G or 3G or their combination.In an exemplary implementation
In example, communication component 316 receives broadcast singal or broadcast related information from external broadcasting management system via broadcast channel.
In one exemplary embodiment, the communication component 316 further includes near-field communication (NFC) module, to promote short range communication.Example
Such as, NFC module can be based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra wide band (UWB) technology,
Bluetooth (BT) technology and other technologies are realized.
In the exemplary embodiment, device 300 can be believed by one or more application specific integrated circuit (ASIC), number
Number processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array
(FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for executing the above method.
In the exemplary embodiment, a kind of non-transitorycomputer readable storage medium including instruction, example are additionally provided
It such as include the memory 304 of instruction, above-metioned instruction can be executed by the processor 320 of device 300 to complete the above method.For example,
The non-transitorycomputer readable storage medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk
With optical data storage devices etc..
A kind of non-transitorycomputer readable storage medium, when the instruction in the storage medium is by the processing of mobile terminal
When device executes, so that mobile terminal is able to carry out a kind of problem generation method, which comprises
Obtain historical problem；
Feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem；
Knowledge point is extracted from the answer of the historical problem according to the feature extraction rule；
Problem is generated according to the knowledge point.
Fig. 4 is the structural schematic diagram of server in the embodiment of the present invention.The server 400 can be due to configuration or performance be different
Generate bigger difference, may include one or more central processing units (central processing units,
CPU) 422 (for example, one or more processors) and memory 432, one or more storage application programs 442 or
The storage medium 430 (such as one or more mass memory units) of data 444.Wherein, memory 432 and storage medium
430 can be of short duration storage or persistent storage.The program for being stored in storage medium 430 may include one or more modules
(diagram does not mark), each module may include to the series of instructions operation in server.Further, central processing unit
422 can be set to communicate with storage medium 430, and the series of instructions behaviour in storage medium 430 is executed on server 400
Make.
Server 400 can also include one or more power supplys 424, one or more wired or wireless networks
Interface 450, one or more input/output interfaces 458, one or more keyboards 454, and/or, one or one
The above operating system 441, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM etc..
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and foregoing routine can be stored in a computer readable storage medium, which exists
When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned can be at least one in following media
Kind: read-only memory (English: read-only memory, abbreviation: ROM), RAM, magnetic or disk etc. are various to be can store
The medium of program code.
It should be noted that all the embodiments in this specification are described in a progressive manner, each embodiment it
Between same and similar part may refer to each other, each embodiment focuses on the differences from other embodiments.
For equipment and system embodiment, since it is substantially similar to the method embodiment, so describe fairly simple,
The relevent part can refer to the partial explaination of embodiments of method.Equipment and system embodiment described above is only schematic
, wherein unit may or may not be physically separated as illustrated by the separation member, it is shown as a unit
Component may or may not be physical unit, it can and it is in one place, or may be distributed over multiple networks
On unit.Some or all of the modules therein can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
Those of ordinary skill in the art can understand and implement without creative efforts.
The above, only a kind of specific embodiment of the application, but the protection scope of the application is not limited thereto,
Within the technical scope of the present application, any changes or substitutions that can be easily thought of by anyone skilled in the art,
Should all it cover within the scope of protection of this application.Therefore, the protection scope of the application should be with scope of protection of the claims
Subject to.
Claims (10)
1. a kind of problem generation method, which is characterized in that the described method includes:
Obtain historical problem；
Feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem；
Knowledge point is extracted from the answer of the historical problem according to the feature extraction rule；
Problem is generated according to the knowledge point.
2. the method according to claim 1, wherein described ask according to the feature extraction rule from the history
Knowledge point is extracted in the answer of topic, comprising:
Meta-knoeledge is extracted from the answer of the historical problem according to the feature extraction rule, the meta-knoeledge is for embodying
In the answer of the historical problem with the knowledge content of the Knowledge Relation；
The knowledge point is determined according to the answer of the meta-knoeledge and the historical problem.
3. according to the method described in claim 2, it is characterized in that, described generate problem according to the knowledge point, comprising:
According to the incidence relation of the meta-knoeledge and the knowledge point, corresponding enquirement mode is selected；
Described problem is generated according to selected enquirement mode and the knowledge point.
4. according to the method described in claim 3, it is characterized in that, it is described according to the knowledge point generation problem after, also
Include:
It is that the described problem generated determines corresponding answer according to the meta-knoeledge.
5. the method according to claim 1, wherein described ask according to the feature extraction rule from the history
Knowledge point is extracted in the answer of topic, comprising:
Knowledge point is extracted from the answer of the historical problem in conjunction with the historical problem according to the feature extraction rule.
6. method described according to claim 1 or 2 or 5, which is characterized in that described to generate problem, packet according to the knowledge point
It includes:
Problem is generated according to preset enquirement mode and the knowledge point.
7. a kind of problem generating means, which is characterized in that described device includes acquiring unit, determination unit, extraction unit and life
At unit:
The acquiring unit, for obtaining historical problem；
The determination unit, for determining that feature corresponding with the enquirement type mentions according to the enquirement type of the historical problem
Take rule；
The extraction unit, for extracting knowledge point from the answer of the historical problem according to the feature extraction rule；
The generation unit, for generating problem according to the knowledge point.
8. device according to claim 7, which is characterized in that the extraction unit include: meta-knoeledge extract subelement and
Knowledge point determines subelement；
The meta-knoeledge extracts subelement, for being extracted from the answer of the historical problem according to the feature extraction rule
Meta-knoeledge, the meta-knoeledge are used to embody the knowledge content in the answer of the historical problem with the Knowledge Relation；
The knowledge point determines subelement, for determining the knowledge according to the answer of the meta-knoeledge and the historical problem
Point.
9. a kind of processing equipment generated for problem, which is characterized in that include memory and one or more than one
Program, one of them perhaps more than one program be stored in memory and be configured to by one or more than one
Managing device and executing the one or more programs includes the instruction for performing the following operation:
Obtain historical problem；
Feature extraction rule corresponding with the enquirement type is determined according to the enquirement type of the historical problem；
Knowledge point is extracted from the answer of the historical problem according to the feature extraction rule；
Problem is generated according to the knowledge point.
10. a kind of machine readable media is stored thereon with instruction, when executed by one or more processors, so that device is held
Problem generation method of the row as described in one or more in claim 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711092223.5A CN110019706B (en) | 2017-11-08 | 2017-11-08 | Question generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711092223.5A CN110019706B (en) | 2017-11-08 | 2017-11-08 | Question generation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110019706A true CN110019706A (en) | 2019-07-16 |
| CN110019706B CN110019706B (en) | 2021-11-19 |
Family
ID=67186774
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711092223.5A Active CN110019706B (en) | 2017-11-08 | 2017-11-08 | Question generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110019706B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110378818A (en) * | 2019-07-22 | 2019-10-25 | 广西大学 | Personalized exercise recommended method, system and medium based on difficulty |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103902652A (en) * | 2014-02-27 | 2014-07-02 | 深圳市智搜信息技术有限公司 | Automatic question-answering system |
| CN104991965A (en) * | 2015-07-23 | 2015-10-21 | 上海智臻网络科技有限公司 | Method and apparatus for creating extended question based on standard question |
| CN105608199A (en) * | 2015-12-25 | 2016-05-25 | 上海智臻智能网络科技股份有限公司 | Extension method and device for standard questions in intelligent question answering system |
-
2017
- 2017-11-08 CN CN201711092223.5A patent/CN110019706B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103902652A (en) * | 2014-02-27 | 2014-07-02 | 深圳市智搜信息技术有限公司 | Automatic question-answering system |
| CN104991965A (en) * | 2015-07-23 | 2015-10-21 | 上海智臻网络科技有限公司 | Method and apparatus for creating extended question based on standard question |
| CN105608199A (en) * | 2015-12-25 | 2016-05-25 | 上海智臻智能网络科技股份有限公司 | Extension method and device for standard questions in intelligent question answering system |
Non-Patent Citations (1)
| Title |
|---|
| CHEN等: ""Generating Questions Automatically from Informational Text"", 《PROCEEDINGS OF THE 2ND WORKSHOP ON QUESTION GENERATION》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110378818A (en) * | 2019-07-22 | 2019-10-25 | 广西大学 | Personalized exercise recommended method, system and medium based on difficulty |
| CN110378818B (en) * | 2019-07-22 | 2022-03-11 | 广西大学 | Personalized exercise recommendation method, system and medium based on difficulty |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110019706B (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10191718B2 (en) | Electronic device for processing multi-modal input, method for processing multi-modal input and server for processing multi-modal input | |
| EP3163569B1 (en) | Method and device for controlling a smart device by voice, computer program and recording medium | |
| JP2019117623A (en) | Voice dialogue method, apparatus, device and storage medium | |
| US8995625B2 (en) | Unified interface and routing module for handling audio input | |
| JP7166294B2 (en) | Audio processing method, device and storage medium | |
| CN108108142A (en) | Voice information processing method, device, terminal device and storage medium | |
| EP3933570A1 (en) | Method and apparatus for controlling a voice assistant, and computer-readable storage medium | |
| CN105224601B (en) | A kind of method and apparatus of extracting time information | |
| CN105389304A (en) | Event extraction method and apparatus | |
| CN104035995A (en) | Method and device for generating group tags | |
| US20200163038A1 (en) | Method and device for selecting from a plurality of beams | |
| CN110349578A (en) | Equipment wakes up processing method and processing device | |
| EP3734472A1 (en) | Method and device for text processing | |
| CN116166843B (en) | Text video cross-modal retrieval method and device based on fine granularity perception | |
| CN111369994B (en) | Voice processing method and electronic equipment | |
| CN109256145A (en) | Audio-frequency processing method, device, terminal and readable storage medium storing program for executing based on terminal | |
| CN109325337A (en) | Unlocking method and device | |
| CN109670025A (en) | Dialogue management method and device | |
| CN110019706A (en) | A kind of problem generation method and device | |
| CN109977424A (en) | A kind of training method and device of Machine Translation Model | |
| CN110012359A (en) | Answer reminding method and device | |
| CN110046276A (en) | The search method and device of keyword in a kind of voice | |
| CN109120793A (en) | Method of speech processing and device | |
| CN105094891B (en) | effect display method and device | |
| CN111968680A (en) | Voice processing method, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |