CN109359290B - Knowledge point determining method of test question text, electronic equipment and storage medium - Google Patents
Knowledge point determining method of test question text, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a knowledge point determining method of test question text, electronic equipment and a storage medium. The method comprises the steps of reading a preset knowledge point list; word segmentation processing is carried out on the test question text to obtain a plurality of word segments; determining a first type word segmentation set and a second type word segmentation set according to the knowledge point list, wherein the first type word segmentation set is a word segmentation set which is contained in a preset knowledge point list in a plurality of words, and the second type word segmentation set is a word segmentation set which is not contained in the preset knowledge point list in a plurality of words; if the first type word segmentation set is not an empty set, determining that one word segmentation in the first type word segmentation set is used as a knowledge point of the test question text according to a preset rule; if the first class word segmentation set is an empty set, determining that the word segmentation in the second class word segmentation set, which is the highest in frequency and is contained in a preset keyword list, is used as a knowledge point of the test question text, wherein the keyword list is larger than the knowledge point list. At least partially solving the problems of difficult modeling, complex algorithm and low accuracy in test question text analysis.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to a knowledge point determining method of test question text, electronic equipment and a storage medium.
Background
The question bank data is an important teaching resource, and knowledge point data items of questions in the question bank play a very important role, and are important fields for classifying and searching the question bank and are the basis of various extension applications. In practical middle examination and high examination, the scoring of the student test paper also follows a principle of 'stepping point scoring', namely, the score of the corresponding part can be obtained even if the questions are not solved as long as the examined knowledge points are written according to the steps.
However, the generation of knowledge points is a very labor-consuming matter, and a teacher with rich teaching experience is usually required to list the knowledge points according to questions, and the knowledge points are subjected to links such as auditing, input, checking and the like and then put in storage, so that the production of the knowledge points of each question takes 2 to 3 yuan. Today, the number of question banks is very small in millions or even tens of millions, which is definitely a huge investment; at the same time, the organization, management and time-consuming construction period of personnel required in real operation are all headache matters.
Patent document CN106570109a discloses a method of automatically generating question bank knowledge points by text analysis. However, the method still has the problems of difficult modeling, complex algorithm and insufficient accuracy.
Disclosure of Invention
The application provides a knowledge point determining method of test question text, electronic equipment and a storage medium, so as to at least partially solve the problems of difficult modeling, complex algorithm and low accuracy in question text analysis.
The method for determining the knowledge points of the test question text is characterized by comprising the following steps:
reading a preset knowledge point list;
word segmentation processing is carried out on the test question text to obtain a plurality of word segments;
determining a first type word segmentation set and a second type word segmentation set according to the knowledge point list, wherein the first type word segmentation set is a word segmentation set which is contained in the preset knowledge point list in the plurality of words, and the second type word segmentation set is a word segmentation set which is not contained in the preset knowledge point list in the plurality of words;
if the first type word segmentation set is not an empty set, determining a word segmentation in the first type word segmentation set as a knowledge point of the test question text according to a preset rule;
if the first class word segmentation set is an empty set, determining that the word segmentation with the highest frequency in the second class word segmentation set and contained in a preset keyword list is used as a knowledge point of the test question text, wherein the keyword list is larger than the knowledge point list.
Optionally, the knowledge point list includes a plurality of first-class knowledge points corresponding to subjects of the test question text.
Optionally, the keyword list includes a plurality of second-class knowledge points corresponding to subjects of the test question text, where the frequency of occurrence of the first-class knowledge points in the subjects is greater than the frequency of occurrence of the second-class knowledge points in the subjects.
Optionally, the word segmentation processing is performed on the test question text to obtain a plurality of segmented words, including:
and performing word segmentation processing on the test question text based on statistical machine learning to obtain a plurality of segmented words.
Optionally, the determining, according to a preset rule, that a word in the first type word segmentation set is used as a knowledge point of the test question text includes:
and determining the word with the highest occurrence frequency in the first type word segmentation set as a knowledge point of the test question text.
Optionally, the determining, according to a preset rule, that a word in the first type word segmentation set is used as a knowledge point of the test question text includes:
and determining at least one word in the first type word segmentation set, the occurrence frequency of which reaches a preset value, as a knowledge point of the test question text.
Optionally, the method includes:
and storing the corresponding relation between the knowledge points and the test question text.
Optionally, the method includes:
acquiring identity information of an examinee;
acquiring corresponding assessment knowledge points according to the identity;
and acquiring corresponding test question text according to the assessment knowledge points.
The application also provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the information processing method of any of the above via execution of the executable instructions.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the information processing method of any of the above.
By adopting the knowledge point determining method of the test question text, the problems of difficult modeling, complex algorithm and low accuracy in test question text analysis can be at least partially solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of method steps according to one embodiment of the present application;
FIG. 2 is a schematic diagram of determining knowledge points, according to one embodiment of the application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to one embodiment of the present application, a method for determining knowledge points of test questions text is provided, and it should be noted that the steps illustrated in the flowcharts of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different from that herein.
According to one embodiment of the application, the method for determining knowledge points of test question text, as shown in fig. 1 and fig. 2, may include the following steps:
step S110, reading a preset knowledge point list;
step S130, word segmentation processing is carried out on the test question text to obtain a plurality of segmented words;
step S150, determining a first type word segmentation set and a second type word segmentation set according to the knowledge point list, wherein the first type word segmentation set is a word segmentation set which is contained in the preset knowledge point list in the plurality of words, and the second type word segmentation set is a word segmentation set which is not contained in the preset knowledge point list in the plurality of words;
step S170, determining knowledge points from a first class word segmentation set and a second class set, specifically, if the first class word segmentation set is not an empty set, determining a word segmentation in the first class word segmentation set as the knowledge point of the test question text according to a preset rule; if the first class word segmentation set is an empty set, determining that the word segmentation with the highest frequency in the second class word segmentation set and contained in a preset keyword list is used as a knowledge point of the test question text, wherein the keyword list is larger than the knowledge point list.
Reading a preset knowledge point list by a knowledge point determining method of test question text in the present exemplary embodiment; word segmentation processing is carried out on the test question text to obtain a plurality of word segments; determining a first type word segmentation set and a second type word segmentation set according to the knowledge point list, wherein the first type word segmentation set is a word segmentation set which is contained in a preset knowledge point list in a plurality of words, and the second type word segmentation set is a word segmentation set which is not contained in the preset knowledge point list in a plurality of words; if the first type word segmentation set is not an empty set, determining that one word segmentation in the first type word segmentation set is used as a knowledge point of the test question text according to a preset rule; if the first class word segmentation set is an empty set, determining that the word segmentation in the second class word segmentation set, which is the highest in frequency and is contained in a preset keyword list, is used as a knowledge point of the test question text, wherein the keyword list is larger than the knowledge point list. The novel knowledge point determining method for the test question text is provided, so that the problems of difficult modeling, complex algorithm and low accuracy in test question text analysis are at least partially solved.
Next, the information processing method in the present exemplary embodiment will be further described.
In step S110, a preset knowledge point list is read.
The preset knowledge point list is a preset set of descriptors of a plurality of knowledge points, for example, a specific knowledge point list may contain a set of descriptors of a plurality of knowledge points such as "number series, quadratic function" and the like.
In an alternative embodiment, the knowledge point list includes a plurality of first-class knowledge points corresponding to subjects of the test question text. For example, the knowledge point list may be preset for different orders, and more specifically, different knowledge point lists may be preset for different years and different orders. The knowledge point list may be set by the teacher according to the specific situation of the subject. Preferably, the knowledge point list may only include knowledge points common to the subject, for example, the knowledge point list may include 80% coverage knowledge points common to the subject.
In step S130, word segmentation processing is performed on the test question text to obtain a plurality of segmented words.
Because of the specificity of Chinese, chinese does not use spaces as natural separators and words are not separated compared with English. In Chinese, the boundaries of the word and the phrase are fuzzy, and the basic expression units of modern Chinese are the word and are usually double-word or multi-word, but the boundaries of the word and the phrase are difficult to distinguish due to different awareness levels of people.
Chinese word segmentation is the basis of text mining, and for a section of input Chinese, chinese word segmentation is successfully carried out, so that the effect of automatically identifying sentence meaning by a computer can be achieved.
Existing word segmentation algorithms can be divided into three main categories: word segmentation method based on character string matching, word segmentation method based on understanding and word segmentation method based on statistics. According to the combination of the part-of-speech labeling process or not, the method can be divided into a simple word segmentation method and an integrated method combining word segmentation and labeling.
Character matching
The method is also called a mechanical word segmentation method, and is characterized in that a Chinese character string to be analyzed is matched with entries in a 'fully large' machine dictionary according to a certain strategy, and if a certain character string is found in the dictionary, the matching is successful (a word is identified). According to different scanning directions, the string matching word segmentation method can be divided into forward matching and reverse matching; according to the situation of the preferential matching of different lengths, the matching can be divided into maximum (longest) matching and minimum (shortest) matching; several mechanical word segmentation methods are commonly used as follows:
1) Forward maximum matching (left to right direction);
2) Reverse maximum matching (right-to-left direction);
3) Minimum segmentation (minimizing the number of words cut in each sentence);
4) Two-way maximum matching method (scanning from left to right and from right to left)
The above-described various methods may also be combined with each other, and for example, a forward maximum matching method and a reverse maximum matching method may be combined to constitute a bidirectional matching method. Due to the word forming characteristics of Chinese characters, the forward minimum matching and the reverse minimum matching are rarely used. In general, the segmentation accuracy of reverse matching is slightly higher than that of forward matching, and the ambiguity phenomenon is less. The statistics show that the error rate of the pure forward maximum matching is 1/169, and the error rate of the pure reverse maximum matching is 1/245. But this accuracy is far from meeting practical requirements. The word segmentation system actually used takes mechanical word segmentation as a primary segmentation means, and further improves the segmentation accuracy by utilizing various other language information.
One method is to improve the scanning mode, called feature scanning or sign segmentation, to identify and segment some words with obvious features in the character string to be analyzed, and to use these words as break points to divide the original character string into smaller strings and then to enter mechanical word segmentation, so as to reduce the error rate of matching. The other method combines word segmentation and word class labeling, provides help for word segmentation decision by using rich word class information, and also carries out inspection and adjustment on word segmentation results in the labeling process, thereby greatly improving the segmentation accuracy.
For the mechanical word segmentation method, a general model can be built, and a professional academic paper is provided in this respect, which is not discussed in detail here.
Method of decomposition
The word segmentation method achieves the effect of word recognition by enabling a computer to simulate the understanding of people to sentences. The basic idea is that the syntactic and semantic analysis is performed while the words are segmented, and the syntactic information and the semantic information are utilized to process the ambiguity. It generally consists of three parts: the system comprises a word segmentation subsystem, a syntactic semantic subsystem and a general control part. Under the coordination of the general control part, the word segmentation subsystem can obtain the syntactic and semantic information of related words, sentences and the like to judge word segmentation ambiguity, namely, the word segmentation subsystem simulates the understanding process of people to sentences. This word segmentation method requires the use of a large amount of language knowledge and information. Because of the general and complex nature of Chinese language knowledge, it is difficult to organize various language information into machine-readable forms, and word segmentation systems based on understanding are still in the experimental stage at present.
Statistical method
Formally, words are stable combinations of words, and therefore in this context, the more times adjacent words appear simultaneously, the more likely a word is composed. Therefore, the frequency or probability of co-occurrence of the characters adjacent to the characters can better reflect the credibility of the formed words. The frequency of the combination of each word of adjacent co-occurrence in the corpus can be counted, and the co-occurrence information of each word can be calculated. And defining mutual occurrence information of the two words, and calculating adjacent co-occurrence probabilities of the two Chinese characters X, Y. The mutual information shows the tightness of the combination relation between Chinese characters. When the degree of compactness is above a certain threshold, it is considered that the word may constitute a word. The method only needs to count the word group frequency in the corpus, and does not need to split a dictionary, so the method is also called a dictionary-free word segmentation method or a statistical word extraction method. However, this method has a certain limitation that common word groups, such as "this", "one", "some", "my", "many", etc., which are frequently found but are not words, are frequently extracted, and recognition accuracy of the common words is poor and space-time overhead is high. The statistical word segmentation system in practical application uses a part of basic word segmentation dictionary (commonly used word dictionary) to carry out string matching word segmentation, and simultaneously uses a statistical method to recognize some new words, namely, the string frequency statistics and the string matching are combined, so that the characteristics of high word segmentation speed and high efficiency of matching word segmentation are brought into play, and the advantages of word segmentation without dictionary are utilized to combine with context recognition word generation and automatic disambiguation.
In this embodiment, word segmentation processing is performed on the test question text based on statistical machine learning to obtain a plurality of segmented words. Firstly, a large number of segmented texts are given, and a statistical machine learning model is utilized to learn word segmentation rules (called training), so that unknown texts are segmented. The ability of each word in Chinese to be used as a word alone is different, and in addition, some words often appear as prefixes, some words often appear as suffixes, and information about whether the two word phases are temporarily formed or not is combined, so that a lot of knowledge related to word segmentation is obtained. The method is trained by fully utilizing the rule of Chinese word formation to divide words, and the method is time-consuming in the training process, but can improve the word dividing accuracy when being specifically applied to the word dividing process.
In step S150, a first type word segmentation set and a second type word segmentation set are determined according to the knowledge point list, where the first type word segmentation set is a word segmentation set included in the preset knowledge point list in the plurality of words, and the second type word segmentation set is a word segmentation set not included in the preset knowledge point list in the plurality of words.
After word segmentation processing is carried out on the test question text, a plurality of words contained in the preset knowledge point list are used as a first type word segmentation set, and a plurality of words which are not contained in the preset knowledge point list are used as a second type word segmentation set.
In step S170, knowledge points are determined from the first class word set and the second class set, specifically, if the first class word set is not an empty set, determining a word in the first class word set as a knowledge point of the test question text according to a preset rule; if the first class word segmentation set is an empty set, determining that the word segmentation with the highest frequency in the second class word segmentation set and contained in a preset keyword list is used as a knowledge point of the test question text, wherein the keyword list is larger than the knowledge point list.
The specific form of the keyword list may be the same as the above knowledge point list, and the keyword list includes a plurality of second-class knowledge points corresponding to subjects of the test question text, where the frequency of occurrence of the first-class knowledge points in the subjects is greater than the frequency of occurrence of the second-class knowledge points in the subjects. The keyword list may be a collection of descriptors that cover unusual knowledge points of the subject.
If the first word segmentation set is not an empty set, the test question text is indicated to contain some descriptive words (word segmentation) in a preset knowledge point list. Since the preset knowledge point list is a common knowledge point of the subject, one or more segmentation words can be determined as the knowledge point of the test question text.
In an optional implementation, the determining, according to a preset rule, that a word in the first type word set is used as a knowledge point of the test question text includes: and determining the word with the highest occurrence frequency in the first type word segmentation set as a knowledge point of the test question text. Thus, the word with the highest occurrence frequency can be determined from the first word segmentation set to be used as the knowledge point of the test question text.
In an optional implementation, the determining, according to a preset rule, that a word in the first type word set is used as a knowledge point of the test question text includes: and determining at least one word in the first type word segmentation set, the occurrence frequency of which reaches a preset value, as a knowledge point of the test question text. In this way, the preset value may be a preset frequency value, for example, two times, three times, and specifically, a plurality of segmentation words with more than three times in the first class segmentation word set may be used as the test question text knowledge point.
Thus, two knowledge point lists may be preset, one being a set of common knowledge points (the knowledge point list described above) and the other being a set of unusual knowledge points (the keyword list described above). The first type word segmentation set and the second type word segmentation set are obtained by word segmentation of the test question text, and whether the first type word segmentation set is an empty set is further judged, so that knowledge points are determined in a corresponding mode, as shown in fig. 2. The method can at least solve the problems of difficult modeling, complex algorithm and low accuracy in test question text analysis.
In an alternative embodiment, the method includes: and storing the corresponding relation between the knowledge points and the test question text. In the step, the knowledge points corresponding to the test question text are determined, and the corresponding relation between the knowledge points and the test question text is stored, so that the subsequent post-processing of the test question text can be facilitated.
In an alternative embodiment, the method includes: acquiring identity information of an examinee; acquiring corresponding assessment knowledge points according to the identity; and acquiring corresponding test question text according to the assessment knowledge points. In this way, different knowledge points can be pushed according to the identity of the user; the learning behavior of the user, including learning duration, learning content and the like, is tracked through the interactive modes such as popup windows and the like, so that corresponding assessment knowledge points of the examinees can be conveniently obtained according to different identities of the examinees, and personalized examination questions can be conveniently conducted.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the present application may be implemented as a system, method, or program product. Accordingly, aspects of the present application may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
The electronic device of this embodiment of the present application is in the form of a general purpose computing device. Components of an electronic device may include, but are not limited to: the system comprises at least one processing unit, at least one storage unit, a bus for connecting different system components (comprising the storage unit and the processing unit) and a display unit.
Wherein the storage unit stores program code executable by the processing unit such that the processing unit performs the steps described above in the present specification according to various exemplary embodiments of the present application. The memory unit may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) and/or cache memory units, and may further include Read Only Memory (ROM).
The storage unit may also include a program/utility having a set (at least one) of program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The bus may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any device (e.g., router, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. And, the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter. As shown, the network adapter communicates with other modules of the electronic device over a bus.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the present application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the present application as described herein above, when the program product is run on the terminal device.
The program product for implementing the above method according to the embodiment of the present application may employ a portable compact disc read-only memory (CD-ROM) and include a program code, and may be run on a terminal device such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described figures are only illustrative of the processes involved in the method according to exemplary embodiments of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. The method for determining the knowledge points of the test question text is characterized by comprising the following steps:
reading a preset knowledge point list;
word segmentation processing is carried out on the test question text to obtain a plurality of word segments;
determining a first type word segmentation set and a second type word segmentation set according to the knowledge point list, wherein the first type word segmentation set is a word segmentation set which is contained in the preset knowledge point list in the plurality of words, and the second type word segmentation set is a word segmentation set which is not contained in the preset knowledge point list in the plurality of words;
if the first type word segmentation set is not an empty set, determining a word segmentation in the first type word segmentation set as a knowledge point of the test question text according to a preset rule;
if the first class word segmentation set is an empty set, determining that the word segmentation set with the highest frequency in the second class word segmentation set and contained in a preset keyword list is used as a knowledge point of the test question text, wherein the number of elements in the keyword list is larger than that of elements in the knowledge point list.
2. The method of claim 1, wherein the knowledge points list comprises a plurality of first type knowledge points corresponding to subjects of the test question text.
3. The method of claim 2, wherein the keyword list includes a plurality of second-type knowledge points corresponding to subjects of the test question text, wherein the frequency of occurrence of the first-type knowledge points in the subjects is greater than the frequency of occurrence of the second-type knowledge points in the subjects.
4. The method of claim 1, wherein the word segmentation of the test question text to obtain a plurality of segmented words comprises:
and performing word segmentation processing on the test question text based on statistical machine learning to obtain a plurality of segmented words.
5. The method according to claim 1, wherein the determining, according to a preset rule, a word in the first word segment set as a knowledge point of the test question text includes:
and determining the word with the highest occurrence frequency in the first type word segmentation set as a knowledge point of the test question text.
6. The method according to claim 1, wherein the determining, according to a preset rule, a word in the first word segment set as a knowledge point of the test question text includes:
and determining at least one word in the first type word segmentation set, the occurrence frequency of which reaches a preset value, as a knowledge point of the test question text.
7. The method according to claim 1, characterized in that the method comprises:
and storing the corresponding relation between the knowledge points and the test question text.
8. The method according to claim 1, characterized in that the method comprises:
acquiring identity information of an examinee;
acquiring corresponding assessment knowledge points according to the identity information;
and acquiring corresponding test question text according to the assessment knowledge points.
9. An electronic device, comprising:
a processor; the method comprises the steps of,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the knowledge point determination method of test question text of any one of claims 1-8 via execution of the executable instructions.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the knowledge point determination method of test question text according to any one of claims 1-8.
Priority Applications (1)
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| 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 |
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| 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 |
| 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 |
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