CN109359290A - The knowledge point of examination question text determines method, electronic equipment and storage medium - Google Patents
The knowledge point of examination question text determines method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN109359290A CN109359290A CN201810947324.4A CN201810947324A CN109359290A CN 109359290 A CN109359290 A CN 109359290A CN 201810947324 A CN201810947324 A CN 201810947324A CN 109359290 A CN109359290 A CN 109359290A
- Authority
- CN
- China
- Prior art keywords
- knowledge point
- participle
- examination question
- question text
- preset
- 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
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Machine Translation (AREA)
Abstract
This application discloses a kind of knowledge points of examination question text to determine method, electronic equipment and storage medium.This method includes reading preset knowledge point list；Word segmentation processing is carried out to obtain multiple participles to examination question text；First kind participle set and the second class participle set are determined according to knowledge point list, wherein, first kind participle collects the participle set being combined into multiple participles include in preset knowledge point list, and the second class participle collection is combined into the participle set being not included in preset knowledge point list in multiple participles；If first kind participle set is not null set, knowledge point of the participle as examination question text in first kind participle set is determined according to preset rules；If first kind participle collection is combined into null set, determines frequency highest in the second class participle set and include knowledge point of the participle as examination question text in preset lists of keywords, wherein lists of keywords is greater than knowledge point list.Problem difficult, that algorithm is complicated, accuracy is not high is modeled at least to be partially solved in examination question text analyzing.
Description
Technical field
This application involves artificial intelligence fields, determine method, electronics in particular to a kind of knowledge point of examination question text
Equipment and storage medium.
Background technique
Exam pool data are important teaching resource, and the knowledge point data item of topic then has highly important work in exam pool
With it is the significant field of exam pool systematic searching and the basis of various extension applications.It examines in actual in college entrance examination, learns
The marking of raw paper, and in the principle for following one " stepping on a score ", as long as the knowledge point investigated is write out by step,
Even if inscribing no solution to come out, the score of corresponding portion can be also obtained.
However the generation of knowledge point is one takes very much artificial thing, it usually needs experience with students teacher abundant is according to topic
Meaning lists knowledge point, is put in storage again after the links such as audit, typing and check and correction, and the knowledge point production of topic each in this way will spend 2
To 3 yuan of cost.In exam pool quantity easily with today of millions of or even up to ten million scales, this be undoubtedly one it is costly
Investment；Meanwhile personnel arrangement, management and the duration time-consuming needed when really operating is all troubling thing.
Patent document CN106570109A discloses a kind of method for automatically generating exam pool knowledge point by text analyzing.But
It is still may there is a problem of that modeling comparison is difficult, algorithm is complicated, accuracy is not high enough using this method.
Summary of the invention
This application provides a kind of knowledge points of examination question text to determine method, electronic equipment and storage medium, at least portion
Ground is divided to solve the problems, such as that modeling difficulty in topic text analyzing, algorithm complexity, accuracy be not high.
A kind of knowledge point of examination question text determines method, which is characterized in that the described method includes:
Read preset knowledge point list；
Word segmentation processing is carried out to obtain multiple participles to the examination question text；
First kind participle set and the second class participle set are determined according to the knowledge point list, wherein the first kind
Participle collects the participle set being combined into the multiple participle include in the preset knowledge point list, the second class participle
Collection is combined into the participle set being not included in the preset knowledge point list in the multiple participle；
If the first kind participle set is not null set, determined in the first kind participle set according to preset rules
Knowledge point of one participle as the examination question text；
If the first kind participle collection is combined into null set, determines frequency highest in the second class participle set and be included in
Knowledge point of the participle as the examination question text in preset lists of keywords, wherein the lists of keywords is greater than described
Knowledge point list.
It optionally, include multiple first kind knowledge corresponding with the subject of the examination question text in the knowledge point list
Point.
It optionally, include multiple second class knowledge corresponding with the subject of the examination question text in the lists of keywords
Point, wherein the frequency that the first kind knowledge point occurs in the subject is greater than second class knowledge point in the subject
The frequency of middle appearance.
It is optionally, described that word segmentation processing is carried out to obtain multiple participles to the examination question text, comprising:
Word segmentation processing is carried out to obtain multiple participles to the examination question text based on statistical machine learning.
Optionally, described to determine that the participle in the first kind participle set is used as the examination question text according to preset rules
Knowledge point, comprising:
Determine knowledge point of the highest participle of frequency of occurrence as the examination question text in the first kind participle set.
Optionally, described to determine that the participle in the first kind participle set is used as the examination question text according to preset rules
Knowledge point, comprising:
At least one participle for determining that frequency of occurrence reaches preset value in the first kind participle set is used as the examination question
The knowledge point of text.
Optionally, which comprises
Store the corresponding relationship of the knowledge point Yu the examination question text.
Optionally, which comprises
Obtain the identity information of examinee；
Corresponding examination knowledge point is obtained according to the identity；
Corresponding examination question text is obtained according to the examination knowledge point.
Present invention also provides a kind of electronic equipment, comprising: processor；And memory, for storage processor can
It executes instruction；Wherein, processor is configured to execute the information processing method of any of the above item via executable instruction is executed.
Present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, computer program
The information processing method of any of the above item is realized when being executed by processor.
Method is determined by using the knowledge point of examination question text described herein, can at least be partially solved examination question text
Problem difficult, that algorithm is complicated, accuracy is not high is modeled in this analysis.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
Fig. 1 is the method step flow diagram according to the application wherein embodiment；
Fig. 2 is the schematic diagram according to the application wherein determination knowledge point of an embodiment.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to embodiments herein described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
According to the application, wherein an embodiment, the knowledge point for providing a kind of examination question text determine method, need to illustrate
It is that step shown in the flowchart of the accompanying drawings can execute in a computer system such as a set of computer executable instructions,
Also, although logical order is shown in flow charts, and it in some cases, can be to be different from sequence execution herein
Shown or described step.
Method, as depicted in figs. 1 and 2, the party are determined according to the knowledge point of the application wherein examination question text of an embodiment
Method may include steps of:
Step S110 reads preset knowledge point list；
Step S130 carries out word segmentation processing to the examination question text to obtain multiple participles；
Step S150 determines first kind participle set and the second class participle set according to the knowledge point list, wherein institute
Stating first kind participle collection and being combined into the multiple participle includes participle set in the preset knowledge point list, and described the
Two classes participle collection is combined into the participle set being not included in the preset knowledge point list in the multiple participle；
Step S170 determines knowledge point from first kind participle set and the second class set, specifically, if the first kind
Participle set is not null set, determines the participle in the first kind participle set as the examination question text according to preset rules
This knowledge point；If the first kind participle collection is combined into null set, frequency highest and packet in the second class participle set are determined
It is contained in knowledge point of the participle as the examination question text in preset lists of keywords, wherein the lists of keywords is greater than
The knowledge point list.
Method is determined by the knowledge point of the examination question text in the present exemplary embodiment, reads preset knowledge point list；
Word segmentation processing is carried out to obtain multiple participles to examination question text；First kind participle set and the second class are determined according to knowledge point list
Participle set, wherein first kind participle collects the participle set being combined into multiple participles include in preset knowledge point list, the
Two classes participle collection is combined into the participle set being not included in preset knowledge point list in multiple participles；If first kind participle set
It is not null set, knowledge point of the participle as examination question text in first kind participle set is determined according to preset rules；If the
One kind participle collection is combined into null set, determines frequency highest in the second class participle set and includes in preset lists of keywords
Segment the knowledge point as examination question text, wherein lists of keywords is greater than knowledge point list.Provide a kind of new examination question text
Knowledge point determine method, model that difficult, algorithm is complicated, accuracy is not high at least to be partially solved in examination question text analyzing
Problem.
In the following, will be further described to information processing method in the present exemplary embodiment.
In step s 110, preset knowledge point list is read.
Preset knowledge point list is the set of the descriptor of preset multiple knowledge points, for example, a specific knowledge
Point list may include the set of the descriptor of the knowledge point such as " ordered series of numbers, quadratic function ".
In an alternate embodiment of the invention, corresponding with the subject of the examination question text multiple the are included in the knowledge point list
A kind of knowledge point.For example, can be for the default setting knowledge point list of different subjects, it more specifically, can also be for not the same year
Grade and different subject presets different knowledge point lists.Knowledge point list can by teacher according to section's purpose concrete condition and
Setting.Preferably, above-mentioned knowledge point list can only include the common knowledge point of the subject, for example, above-mentioned knowledge point list can
With the knowledge point comprising common 80% covering of the subject.
In step s 130, word segmentation processing is carried out to obtain multiple participles to the examination question text.
Due to the particularity of Chinese, compared to English, Chinese does not have between word not using space as natural separator
There is separation.Also, in Chinese, " word " and " phrase " obscurity boundary, although the primary expression unit of Modern Chinese is " word ", and
It is in the majority with double word or multi-character words, but due to the difference of people's level of understanding, the boundary of word and phrase is very difficult to distinguish.
Chinese word segmentation is the basis of text mining, for one section of Chinese of input, successfully carries out Chinese word segmentation, Ke Yida
To the effect of computer automatic identification sentence meaning.
Existing segmentation methods can be divided into three categories: the segmenting method based on string matching, the participle side based on understanding
Method and segmenting method based on statistics.Combined according to whether with part-of-speech tagging process, but can be divided into simple segmenting method and
Segment the integral method combined with mark.
Character match
This method, which is called, does mechanical segmentation method, it is the Chinese character string being analysed to according to certain strategy and one
Entry in " sufficiently big " machine dictionary is matched, if finding some character string in dictionary, successful match (identifies one
A word).According to the difference of scanning direction, String matching segmenting method can be divided into positive matching and reverse matching；According to different length
The case where spending priority match can be divided into maximum (longest) matching and minimum (most short) matching；Common several mechanical Chinese word segmentation sides
Method is as follows:
1) Forward Maximum Method method (by left-to-right direction)；
2) reverse maximum matching method (by right to left direction)；
3) minimum cutting (keeping the word number cut out in each sentence minimum)；
4) two-way maximum matching method (carry out by it is left-to-right, by right to left twice sweep)
Above-mentioned various methods can also be combined with each other, for example, can be by Forward Maximum Method method and reverse maximum
Method of completing the square, which combines, constitutes bi-directional matching method.The characteristics of due to Chinese word at word, positive smallest match and reverse minimum
With being generally rarely employed.It is, in general, that reverse matched cutting precision is slightly above positive matching, the Ambiguity encountered also compared with
It is few.Statistical result shows the use of the error rate of Forward Maximum Method to be merely 1/169, simple to use reverse maximum matched mistake
Accidentally rate is 1/245.But this precision is also far from satisfying actual needs.The Words partition system of actual use is all mechanical
Participle also needs the accuracy rate by further increasing cutting using various other language messages as a kind of just departure section.
A kind of method is to improve scanning mode, and referred to as mark scanning or mark cutting is preferentially known in character string to be analyzed
Not and some words with obvious characteristic are syncopated as, using these words as breakpoint, former character string can be divided into lesser string and come again
Into mechanical Chinese word segmentation, to reduce matched error rate.Another method is will to segment and part-of-speech tagging combines, using abundant
Grammatical category information provides help to participle decision, and test in annotation process and to word segmentation result, adjust in turn,
To greatly improve the accuracy rate of cutting.
For mechanical segmentation method, a general model can establish, have the academic paper of profession in this respect, here
It is not discussed in detail.
Understanding method
This segmenting method is to achieve the effect that identify word by allowing the understanding of computer mould personification distich.It is basic
Thought is exactly to carry out syntax, semantic analysis while participle, handles Ambiguity using syntactic information and semantic information.It
Generally include three parts: participle subsystem, syntactic-semantic subsystem, master control part.Under the coordination of master control part, participle
System can obtain the syntax and semantic information in relation to word, sentence etc. to judge segmentation ambiguity, i.e., it simulates people couple
The understanding process of sentence.This segmenting method is needed using a large amount of linguistry and information.Due to the cage of Chinese language knowledge
System, complexity, it is difficult to various language messages are organized into the form that machine can be directly read, therefore currently based on the participle of understanding
System is also in experimental stage.
Statistic law
It formally sees, word is stable combinatorics on words, therefore within a context, and the number that adjacent word occurs simultaneously is got over
It is more, it is more possible to constitute a word.What therefore the frequency of word co-occurrence adjacent with word or probability can preferably reflect into word can
Reliability.The frequency of each combinatorics on words of co-occurrence adjacent in corpus can be counted, calculate their information that appears alternatively.Definition
The information that appears alternatively of two words calculates the adjacent co-occurrence probabilities of two Chinese characters X, Y.The information that appears alternatively embodies marriage relation between Chinese character
Tightness degree.When tightness degree is higher than some threshold value, it can think that this word group may constitute a word.This method
The word group frequency in corpus need to only be counted, not need cutting dictionary, thus be called to do no dictionary cutting word method or count and take
Word method.But this method also has certain limitation, can often extract some co-occurrence frequency height out but be not the commonly used word of word
Group, for example, " this ", " one of ", " having ", " I ", " many " etc., and it is poor to the accuracy of identification of everyday words, when be spaced apart
Pin is big.The statistics Words partition system of practical application will use a basic dictionary for word segmentation (everyday words dictionary) to carry out String matching point
Word, while identifying some new words using statistical method, i.e., statistical string frequency and String matching are combined, has both played matching participle
Fast, the high-efficient feature of cutting speed, but be utilized no dictionary cutting word combination context identification new word, automatic disambiguation it is excellent
Point.
In the present embodiment, word segmentation processing is carried out to the examination question text to obtain multiple points based on statistical machine learning
Word.The text largely segmented is provided first, (is referred to as instructed using the rule of statistical machine learning model study word segmentation
Practice), to realize the cutting to unknown text.The ability that each word individually makees word in Chinese is different, the word furthermore having
Occur often as prefix, some words but often as suffix, in conjunction with two words when adjacent whether at word information, thus
Many knowledge related with participle are arrived.The mode of this method training makes full use of the rule of Chinese group word to segment, although
It is time-consuming in the training process, but can be improved the accuracy of participle being specifically applied to participle process.
In step S150, first kind participle set and the second class participle set are determined according to the knowledge point list,
In, the first kind participle collects the participle set being combined into the multiple participle include in the preset knowledge point list,
The second class participle collection is combined into the participle set being not included in the preset knowledge point list in the multiple participle.
It will wherein include multiple points in the preset knowledge point list after carrying out word segmentation processing to examination question text
Word is segmented as the first kind to be gathered, and will wherein be not included in multiple participles in the preset knowledge point list as the second class
Participle set.
In step S170, knowledge point is determined from first kind participle set and the second class set, specifically, if described the
One kind participle set is not null set, determines that the participle in the first kind participle set is used as the examination according to preset rules
Inscribe the knowledge point of text；If the first kind participle collection is combined into null set, frequency highest in the second class participle set is determined
It and include knowledge point of the participle as the examination question text in preset lists of keywords, wherein the lists of keywords
Greater than the knowledge point list.
The concrete form of the lists of keywords can be identical as above-mentioned knowledge point list, includes and institute in lists of keywords
State the corresponding multiple second classes knowledge point of subject of examination question text, wherein the first kind knowledge point occurs in the subject
The frequency be greater than the frequency that occurs in the subject of second class knowledge point.Lists of keywords, which can be, covers section's purpose
The set of the descriptor of uncommon knowledge point.
If the first kind participle set is not null set, show to contain some default knowledge point ranges in examination question text
Descriptor (participle) in table.Since default knowledge point list is the common knowledge point of the subject, in this way, one can therefrom be determined
Knowledge point of a or multiple participles as the examination question text.
It is described to be determined described in the participle conduct in the first kind participle set according to preset rules in optionally implementing
The knowledge point of examination question text, comprising: determine that the highest participle of frequency of occurrence is used as the examination question in the first kind participle set
The knowledge point of text.Determine the highest participle of frequency of occurrence as the examination question text in this way, can segment and gather from the first kind
Knowledge point.
It is described to be determined described in the participle conduct in the first kind participle set according to preset rules in optionally implementing
The knowledge point of examination question text, comprising: determine that the first kind segments at least one point that frequency of occurrence in set reaches preset value
Knowledge point of the word as the examination question text.In this way, preset value can be default frequency value, for example, twice, it is three inferior, specifically
, it can be using the multiple participles occurred in first kind participle set more than three times as the examination question text knowledge point.
In this way, two knowledge point lists can be preset, one be common knowledge point set (above-mentioned knowledge point list),
The other is the set (above-mentioned lists of keywords) of uncommon knowledge point.The first kind is obtained by carrying out participle to examination question text
Participle set and the second participle set, and further judge whether first kind participle set is that null set determines in the corresponding way
Knowledge point, as shown in Figure 2.This method can at least solve to model that difficult, algorithm is complicated, accuracy is not high in examination question text analyzing
The problem of.
In an alternate embodiment of the invention, which comprises store the corresponding relationship of the knowledge point Yu the examination question text.
In above-mentioned steps, it is determined that the corresponding knowledge point of examination question text stores knowledge point pass corresponding with the examination question text
System, can be in order to the subsequent post-processing to examination question text.
In optional implementation column, which comprises obtain the identity information of examinee；According to identity acquisition pair
The examination knowledge point answered；Corresponding examination question text is obtained according to the examination knowledge point.In this way, can be pushed not according to user identity
Same knowledge point；Learning behavior of user, including study duration, learning Content etc. are tracked by interactive modes such as pop-ups, are convenient for
According to the different identity of examinee, its corresponding examination knowledge point is obtained, to carry out personalized examination of setting a question.
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can be realized the above method is additionally provided.
Person of ordinary skill in the field it is understood that the various aspects of the application can be implemented as system, method or
Program product.Therefore, the various aspects of the application can be with specific implementation is as follows, it may be assumed that complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
The electronic equipment of this embodiment of the application is showed in the form of universal computing device.The group of electronic equipment
Part can include but is not limited to: at least one above-mentioned processing unit, at least one above-mentioned storage unit, the different system components of connection
The bus of (including storage unit and processing unit), display unit.
Wherein, the storage unit is stored with program code, and said program code can be executed by the processing unit, make
Obtain the step of processing unit executes the illustrative embodiments various according to the application of this specification foregoing description.Storage is single
Member may include the readable medium of volatile memory cell form, such as Random Access Storage Unit (RAM) and/or cache
Storage unit can further include read-only memory unit (ROM).
Storage unit can also include program/utility with one group of (at least one) program module, such journey
Sequence module includes but is not limited to: operating system, one or more application program, other program modules and program data, this
It may include the realization of network environment in each of a little examples or certain combination.
Bus can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage unit
Controller, peripheral bus, graphics acceleration port, processing unit or the office using any bus structures in a variety of bus structures
Domain bus.
Electronic equipment can also be communicated with one or more external equipments (such as keyboard, sensing equipment, bluetooth equipment etc.),
Can also be enabled a user to one or more equipment interacted with the electronic equipment communication, and/or with make the electronic equipment
Any equipment (such as router, modem etc.) that can be communicated with one or more of the other calculating equipment communicates.
This communication can be carried out by input/output (I/O) interface.Also, electronic equipment can also pass through network adapter and one
A or multiple networks (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet) communication.Such as figure
Shown, network adapter is communicated by bus with other modules of electronic equipment.
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) is executed according to disclosure embodiment
Method.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with
Enough realize the program product of this specification above method.In some possible embodiments, the various aspects of the application may be used also
In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute
State program code for make the terminal device execute this specification foregoing description according to the various exemplary embodiment party of the application
The step of formula.
The program product for realizing the above method of presently filed embodiment, can be using portable compact disc only
It reads memory (CD-ROM) and including program code, and can be run on terminal device, such as PC.However, this Shen
Program product please is without being limited thereto, and in this document, readable storage medium storing program for executing can be any tangible Jie for including or store program
Matter, the program can be commanded execution system, device or device use or in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive
List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing
Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its
The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with any combination of one or more programming languages come write for execute the application operation program
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In addition, above-mentioned attached drawing is only the schematic theory of the processing according to included by the method for the application exemplary embodiment
It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable
Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
It should be noted that although being referred to several modules or list for acting the equipment executed in the above detailed description
Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more
Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould
The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Adaptive change follow the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure or
Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim
It points out.
It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the attached claims.
Claims (10)
1. a kind of knowledge point of examination question text determines method, which is characterized in that the described method includes:
Read preset knowledge point list；
Word segmentation processing is carried out to obtain multiple participles to the examination question text；
First kind participle set and the second class participle set are determined according to the knowledge point list, wherein the first kind participle
Collect the participle set being combined into the multiple participle include in the preset knowledge point list, the second class participle set
For the participle set being not included in the multiple participle in the preset knowledge point list；
If the first kind participle set is not null set, one point in the first kind participle set is determined according to preset rules
Knowledge point of the word as the examination question text；
If the first kind participle collection is combined into null set, determines frequency highest in the second class participle set and be included in default
Lists of keywords in knowledge point of the participle as the examination question text, wherein the lists of keywords is greater than the knowledge
Point list.
2. the method according to claim 1, wherein including and the examination question text in the knowledge point list
The corresponding multiple first kind knowledge points of subject.
3. according to the method described in claim 2, it is characterized in that, including and the examination question text in the lists of keywords
The corresponding multiple second classes knowledge point of subject, wherein the frequency that the first kind knowledge point occurs in the subject is greater than institute
State the frequency that the second class knowledge point occurs in the subject.
4. the method according to claim 1, wherein described carry out word segmentation processing to the examination question text to obtain
Multiple participles, comprising:
Word segmentation processing is carried out to obtain multiple participles to the examination question text based on statistical machine learning.
5. the method according to claim 1, wherein described determine the first kind participle set according to preset rules
In a knowledge point of the participle as the examination question text, comprising:
Determine knowledge point of the highest participle of frequency of occurrence as the examination question text in the first kind participle set.
6. the method according to claim 1, wherein described determine the first kind participle set according to preset rules
In a knowledge point of the participle as the examination question text, comprising:
At least one participle for determining that frequency of occurrence reaches preset value in the first kind participle set is used as the examination question text
Knowledge point.
7. the method according to claim 1, wherein the described method includes:
Store the corresponding relationship of the knowledge point Yu the examination question text.
8. the method according to claim 1, wherein the described method includes:
Obtain the identity information of examinee；
Corresponding examination knowledge point is obtained according to the identity；
Corresponding examination question text is obtained according to the examination knowledge point.
9. a kind of electronic equipment characterized by comprising
Processor；And
Memory, for storing the executable instruction of the processor；
Wherein, the processor is configured to require 1-8 described in any item via executing the executable instruction and carry out perform claim
Information processing method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
Claim 1-8 described in any item information processing methods are realized when being executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810947324.4A CN109359290B (en) | 2018-08-20 | 2018-08-20 | Knowledge point determining method of test question text, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810947324.4A CN109359290B (en) | 2018-08-20 | 2018-08-20 | Knowledge point determining method of test question text, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109359290A true CN109359290A (en) | 2019-02-19 |
| CN109359290B CN109359290B (en) | 2023-05-05 |
Family
ID=65350163
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810947324.4A Active CN109359290B (en) | 2018-08-20 | 2018-08-20 | Knowledge point determining method of test question text, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109359290B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110134945A (en) * | 2019-04-15 | 2019-08-16 | 平安科技（深圳）有限公司 | The recognition methods of exercise examination point, device, equipment and storage medium |
| CN110136513A (en) * | 2019-05-13 | 2019-08-16 | 上海乂学教育科技有限公司 | English Listening Comprehension resolution system |
| CN110852071A (en) * | 2019-11-08 | 2020-02-28 | 科大讯飞股份有限公司 | Knowledge point detection method, device, equipment and readable storage medium |
| CN112286900A (en) * | 2019-07-16 | 2021-01-29 | 北京字节跳动网络技术有限公司 | Data processing method, device, equipment and storage medium |
| CN112395858A (en) * | 2020-11-17 | 2021-02-23 | 华中师范大学 | Multi-knowledge point marking method and system fusing test question data and answer data |
| CN112800182A (en) * | 2021-02-10 | 2021-05-14 | 联想(北京)有限公司 | Test question generation method and device |
| CN114626375A (en) * | 2021-04-21 | 2022-06-14 | 亚信科技（南京）有限公司 | Text word segmentation method and device, electronic equipment and computer readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140316768A1 (en) * | 2012-12-14 | 2014-10-23 | Pramod Khandekar | Systems and methods for natural language processing |
| CN105893363A (en) * | 2014-09-26 | 2016-08-24 | 北大方正集团有限公司 | A method and a system for acquiring relevant knowledge points of a knowledge point |
| CN106156335A (en) * | 2016-07-07 | 2016-11-23 | 苏州大学 | A kind of discovery and arrangement method and system of teaching material knowledge point |
| WO2017057858A1 (en) * | 2015-10-01 | 2017-04-06 | 한국외국어대학교 연구산학협력단 | Knowledge managing system having search function for each of multiple fields by weighted value |
| CN106570109A (en) * | 2016-11-01 | 2017-04-19 | 深圳市前海点通数据有限公司 | Method for automatically generating knowledge points of question bank through text analysis |
| CN107562918A (en) * | 2017-09-12 | 2018-01-09 | 北京点易通科技有限公司 | A kind of mathematical problem knowledge point discovery and batch label acquisition method |
| CN108182177A (en) * | 2018-01-24 | 2018-06-19 | 谢德刚 | A kind of mathematics knowledge-ID automation mask method and device |
-
2018
- 2018-08-20 CN CN201810947324.4A patent/CN109359290B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140316768A1 (en) * | 2012-12-14 | 2014-10-23 | Pramod Khandekar | Systems and methods for natural language processing |
| CN105893363A (en) * | 2014-09-26 | 2016-08-24 | 北大方正集团有限公司 | A method and a system for acquiring relevant knowledge points of a knowledge point |
| WO2017057858A1 (en) * | 2015-10-01 | 2017-04-06 | 한국외국어대학교 연구산학협력단 | Knowledge managing system having search function for each of multiple fields by weighted value |
| CN106156335A (en) * | 2016-07-07 | 2016-11-23 | 苏州大学 | A kind of discovery and arrangement method and system of teaching material knowledge point |
| CN106570109A (en) * | 2016-11-01 | 2017-04-19 | 深圳市前海点通数据有限公司 | Method for automatically generating knowledge points of question bank through text analysis |
| CN107562918A (en) * | 2017-09-12 | 2018-01-09 | 北京点易通科技有限公司 | A kind of mathematical problem knowledge point discovery and batch label acquisition method |
| CN108182177A (en) * | 2018-01-24 | 2018-06-19 | 谢德刚 | A kind of mathematics knowledge-ID automation mask method and device |
Non-Patent Citations (2)
| Title |
|---|
| YOUCHAN ZHU AND FENG SHEN: "An Office Automatic Marking System Research and Implementation", 《JOURNAL OF THEORETICAL AND APPLIED INFORMATION TECHNOLOGY》 * |
| 万永权等: "反馈式个性化试题推荐方法", 《计算机应用与软件》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110134945A (en) * | 2019-04-15 | 2019-08-16 | 平安科技（深圳）有限公司 | The recognition methods of exercise examination point, device, equipment and storage medium |
| CN110134945B (en) * | 2019-04-15 | 2024-04-23 | 平安科技（深圳）有限公司 | Method, device, equipment and storage medium for identifying examination points of exercise |
| CN110136513A (en) * | 2019-05-13 | 2019-08-16 | 上海乂学教育科技有限公司 | English Listening Comprehension resolution system |
| CN112286900A (en) * | 2019-07-16 | 2021-01-29 | 北京字节跳动网络技术有限公司 | Data processing method, device, equipment and storage medium |
| CN110852071A (en) * | 2019-11-08 | 2020-02-28 | 科大讯飞股份有限公司 | Knowledge point detection method, device, equipment and readable storage medium |
| CN110852071B (en) * | 2019-11-08 | 2023-10-24 | 科大讯飞股份有限公司 | Knowledge point detection method, device, equipment and readable storage medium |
| CN112395858A (en) * | 2020-11-17 | 2021-02-23 | 华中师范大学 | Multi-knowledge point marking method and system fusing test question data and answer data |
| CN112395858B (en) * | 2020-11-17 | 2024-04-23 | 华中师范大学 | Multi-knowledge point labeling method and system integrating test question data and answer data |
| CN112800182A (en) * | 2021-02-10 | 2021-05-14 | 联想(北京)有限公司 | Test question generation method and device |
| CN114626375A (en) * | 2021-04-21 | 2022-06-14 | 亚信科技（南京）有限公司 | Text word segmentation method and device, electronic equipment and computer readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109359290B (en) | 2023-05-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109359290A (en) | The knowledge point of examination question text determines method, electronic equipment and storage medium | |
| CN110852087B (en) | Chinese error correction method and device, storage medium and electronic device | |
| KR102491172B1 (en) | Natural language question-answering system and learning method | |
| CN112270196B (en) | Entity relationship identification method and device and electronic equipment | |
| US10140272B2 (en) | Dynamic context aware abbreviation detection and annotation | |
| CN110516067A (en) | Public sentiment monitoring method, system and storage medium based on topic detection | |
| CN111931517B (en) | Text translation method, device, electronic equipment and storage medium | |
| CN107291684B (en) | Word segmentation method and system for language text | |
| US9646512B2 (en) | System and method for automated teaching of languages based on frequency of syntactic models | |
| CN113590810B (en) | Abstract generation model training method, abstract generation device and electronic equipment | |
| CN107943940A (en) | Data processing method, medium, system and electronic equipment | |
| CN116561538A (en) | Question-answer scoring method, question-answer scoring device, electronic equipment and storage medium | |
| CN111369980A (en) | Voice detection method and device, electronic equipment and storage medium | |
| WO2023278052A1 (en) | Automated troubleshooter | |
| CN117271736A (en) | Question-answer pair generation method and system, electronic equipment and storage medium | |
| CN116151220A (en) | Word segmentation model training method, word segmentation processing method and device | |
| CN113139043B (en) | Question-answer sample generation method and device, electronic equipment and storage medium | |
| CN112052424B (en) | Content auditing method and device | |
| EP3846075A1 (en) | Contextualized character recognition system | |
| KR102251554B1 (en) | Method for generating educational foreign language text by adjusting text difficulty | |
| JP6168057B2 (en) | Failure occurrence cause extraction device, failure occurrence cause extraction method, and failure occurrence cause extraction program | |
| CN114722774B (en) | Data compression method, device, electronic equipment and storage medium | |
| CN115034209A (en) | Text analysis method and device, electronic equipment and storage medium | |
| CN114722174A (en) | Word extraction method and device, electronic equipment and storage medium | |
| US20210264113A1 (en) | Automatically extending a domain taxonomy to the level of granularity present in glossaries in documents |
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 |