CN111930792A - Data resource labeling method and device, storage medium and electronic equipment - Google Patents

Abstract

The embodiments of the present application disclose a data resource labeling method, device, storage medium and electronic device, which belong to the technical field of computers. The method includes: the server preprocesses the original data resource to obtain text data, calculates the similarity between the text data and a plurality of target knowledge points respectively to obtain the similarity value, and generates the original data resource according to the comparison result of the similarity value and the similarity threshold. The basic knowledge point label set, according to the characteristic information of the original data resource and the basic knowledge point label set, generate the comprehensive knowledge point label set of the original data resource, which can quickly and accurately label the original data resource with the relevant knowledge point label, improve the Labeling efficiency and labeling accuracy.

Description

Data resource labeling method, device, storage medium and electronic device

technical field

The present application relates to the field of computer technology, and in particular, to a method, device, storage medium and electronic device for labeling data resources.

Background technique

With the development of the Internet, data plays an increasingly important role in the Internet industry. For example, various industries such as retail, transportation, social networking, search, education, and medical care all involve large-scale data analysis. Taking online education as an example, in online education scenarios, staff usually need to analyze the user's teaching data to obtain the user's teaching situation and learning situation, so as to provide better services for the user in the future, and the process of analyzing the user's learning data needs to obtain the user's Knowledge point labels associated with learned data resources, and similar application scenarios are also common in other fields. However, in related technologies, knowledge point labels associated with data resources usually need to be marked in advance by manual methods. This method of labeling knowledge point labels is inefficient and will be affected by the subjective factors of the labeler, resulting in inability to Accurately label data resources with knowledge point labels.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a data resource labeling method, device, storage medium and electronic device, which can solve the problem of inaccurate and low efficiency in labeling knowledge point labels on data resources in the related art.

The technical solution is as follows:

In a first aspect, an embodiment of the present application provides a method for labeling data resources, the method comprising:

Preprocess raw data resources to obtain text data;

Perform similarity calculation with the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein, each of the plurality of target knowledge points is associated with a basic knowledge point label;

The basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: the target whose similarity value is greater than the similarity threshold value Basic knowledge point labels associated with knowledge points;

A comprehensive knowledge point tag set of the original data resource is generated according to the feature information of the original data resource and the basic knowledge point tag set.

In a second aspect, an embodiment of the present application provides a device for labeling data resources, where the device for labeling data resources includes:

The preprocessing module is used to preprocess the original data resources to obtain text data;

a calculation module, configured to calculate the similarity between the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein each of the plurality of target knowledge points is associated with a basic knowledge point label;

The first processing module is configured to generate the basic knowledge point label set of the original data resource according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: similarity The basic knowledge point label associated with the target knowledge point whose value is greater than the similarity threshold;

The second processing module is configured to generate a comprehensive knowledge point tag set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point tag set.

In a third aspect, an embodiment of the present application provides a computer storage medium, where the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

In a fourth aspect, an embodiment of the present application provides an electronic device, which may include: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded by the processor and execute the above method steps .

The beneficial effects brought by the technical solutions provided by some embodiments of the present application include at least:

When the solution of the embodiment of the present application is executed, the server preprocesses the original data resource to obtain text data, and calculates the similarity between the text data and multiple target knowledge points to obtain a similarity value. According to the difference between the similarity value and the similarity threshold The comparison result generates the basic knowledge point label set of the original data resource, and generates the comprehensive knowledge point label set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point label set, which can quickly and accurately label the original data resource with Related knowledge point labels, improve the efficiency of labeling and the accuracy of labeling.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

1 is a system architecture diagram provided by an embodiment of the present application;

2 is a schematic flowchart of a method for labeling data resources provided by an embodiment of the present application;

3 is another schematic flowchart of a method for labeling data resources provided by an embodiment of the present application;

FIG. 4 is a schematic flowchart of similarity calculation of the data resource labeling method provided by the embodiment of the present application;

5 is a schematic structural diagram of a device provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an apparatus provided by an embodiment of the present application.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

FIG. 1 shows a schematic diagram of an exemplary system architecture 100 to which the data resource labeling method or the data resource labeling apparatus according to the embodiments of the present application can be applied.

As shown in FIG. 1 , the system architecture 100 may include one or more of terminal devices 101 , 102 , and 103 , a network 104 and a server 105 . The network 104 is a medium used to provide a communication link between the terminal devices 101, 102, 103 and the server 105. Various communication client applications can be installed on the terminal devices 101, 102, and 103, such as: video recording application, video playback Applications, voice interactive applications, search applications, instant communication tools, email clients, social platform software, etc. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like. The terminal devices 101, 102, 103 may be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, and the like. The network 104 may include various types of wired communication links or wireless communication links, such as: wired communication links include fiber optic, twisted pair, or coaxial cables, wireless communication links include Bluetooth communication links, wireless fidelity ( WIreless-FIdelity, Wi-Fi) communication link or microwave communication link, etc. The terminal devices 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, and 103 are software, they may be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, used to provide distributed services), or can be implemented as a single software or software module, which is not specifically limited here. When the terminal devices 101, 102, and 103 are hardware, a display device and a camera can also be installed thereon. The display device display can be various devices that can realize the display function, and the camera is used to capture video streams; for example, the display device can be a Cathode ray tube display (CR), Light-emitting diode display (LED), E-ink screen, Liquid crystal display (LCD), Plasma display panel (PDP) )Wait. The user can use the display devices on the terminal devices 101 , 102 and 103 to view the displayed text, pictures, videos and other information.

It should be noted that, the method for labeling data resources provided by the embodiments of the present application is usually performed by the server 105 , and correspondingly, the device for labeling data resources is usually set in the server 105 . The server 105 may be a server providing various services, and the server 105 may be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or can be implemented as a single server. When the server 105 is software, it may be implemented as multiple software or software modules (for example, used to provide distributed services), or may be implemented as a single software or software module, which is not specifically limited herein.

The server 105 in this application may be a terminal device that provides various services, such as: the server preprocesses the original data resources to obtain text data, and calculates the similarity between the text data and multiple target knowledge points to obtain the similarity value. The comparison result of the similarity value and the similarity threshold value generates a basic knowledge point label set of the original data resource, and generates a comprehensive knowledge point label set of the original data resource according to the feature information of the original data resource and the basic knowledge point label set.

It should be noted here that the data resource labeling method provided by the embodiments of the present application may be executed by one or more of the terminal devices 101, 102, and 103, and/or the server 105. Correspondingly, the embodiments of the present application The device for marking the provided data resources is generally set in the corresponding terminal device, and/or in the server 105, but the present application is not limited to this.

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

The method for labeling data resources provided by the embodiments of the present application will be described in detail below with reference to FIG. 2 to FIG. 4 . It should be noted here that, for the convenience of description, the embodiments are described by taking the online education industry as an example, but those skilled in the art will understand that the application of this application is not limited to the online education industry. The method can be effectively applied to various fields of the Internet.

Referring to FIG. 2 , a schematic flowchart of a method for labeling data resources is provided in an embodiment of the present application. As shown in FIG. 2 , the method of the embodiment of the present application may include the following steps:

S201, preprocessing the original data resource to obtain text data.

Among them, the original data resources refer to data resources that exist in the form of text, audio, video, etc., such as exercises, picture books, learning audio, learning videos and other data resources, and the original data resources include the corresponding learning levels and subjects. Text data refers to the data after unified conversion of original data resources of text, audio, video and other types into text type.

Generally, when the original data resource is of audio or video type, the original data resource may be converted into text data of a preset text type through an ASR (Automatic Speech Recognition, automatic speech recognition) technology. ASR technology is a technology that converts audio to text based on a list of key words, converts audio (or audio in video) content into speech features through spectrum, and matches the speech features with the entries in the key word list, and The best matching result obtained is used as the recognition result. When the original data resource is a text type but not a preset text type, the text type needs to be converted into a preset text type. Common text types are: txt., doc., hlp., wps., rtf., htm., pdf, etc. The default text types can be set to different text types according to actual needs.

S202: Perform similarity calculation on the text data and a plurality of target knowledge points respectively to obtain a similarity value.

The target knowledge point includes at least one of target content vocabulary, target high-frequency vocabulary, target verb vocabulary, target mathematical vocabulary, target phonetic symbol, target sentence pattern, and target grammar, and is a knowledge point that can be obtained from the knowledge map. Learning levels correspond to different target knowledge points, and multiple target knowledge points are each associated with a basic knowledge point label. The similarity value refers to the similarity relationship between the two quantities being compared. Generally, the larger the similarity value is, the more similar the two quantities are.

Generally, before calculating the similarity between text data and multiple target knowledge points to obtain the similarity value, it is necessary to obtain the course information corresponding to the original data resource, and query the course information from the preset knowledge map to obtain the corresponding multi-level information. A target knowledge point, and the knowledge map contains target knowledge points of different learning levels and different years. The similarity calculation is performed on the text data and multiple target knowledge points respectively, that is, the similarity value between the basic knowledge point in the text data and the target knowledge point is calculated, and the basic knowledge point corresponding to the text data can be determined based on the similarity value. Label, that is, the basic knowledge point label corresponding to the original data resource. The basic knowledge points in the text data may include one or more of reference content vocabulary, reference high frequency vocabulary, reference verb vocabulary, reference mathematical vocabulary, reference phonetic symbol, reference sentence pattern and reference grammar.

S203: Generate a basic knowledge point label set of the original data resource according to the comparison result of the similarity value and the similarity threshold.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the condition, that is, the similarity threshold value. The basic knowledge point tag set is a collection of basic knowledge point tags corresponding to the original data resources, which can include the target content vocabulary, target high-frequency vocabulary, target verb vocabulary, target mathematical vocabulary, target phonetic symbol, target sentence pattern and target grammar. The knowledge point labels of , the basic knowledge point labels included in the basic knowledge point label set are the basic knowledge point labels associated with the target knowledge points whose similarity value is greater than the similarity threshold.

Generally, according to different basic knowledge points and different calculation methods of similarity value, the corresponding similarity value, similarity threshold, and basic knowledge point label are different. When the target knowledge point is the target content vocabulary, according to the similarity degree The comparison result of the value and the similarity threshold generates the basic knowledge point label set of the original data resource, including: performing sentence segmentation on the text data to obtain a sentence set, dividing the sentence set to obtain a word block set, and extracting TF based on keywords. - The IDF algorithm calculates the weight of the importance of each word block in the word block set, uses the word block whose importance weight is greater than the first preset weight as the reference content word, and calculates the similarity value between the reference content word and the target content word , when the similarity value is greater than the similarity threshold, obtain the basic knowledge point labels associated with the target content vocabulary, and add the basic knowledge point labels associated with the target content vocabulary to the basic knowledge point label set.

When the target knowledge point is the target high-frequency vocabulary, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain the sentence set, and analyzing the sentence set. Perform block processing to obtain a word block set, calculate the importance weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm, and use the word block whose importance degree weight is less than or equal to the second preset weight as the word block. Refer to the high-frequency vocabulary, calculate the similarity value between the reference high-frequency vocabulary and the target high-frequency vocabulary, and when the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target high-frequency vocabulary, and associate the target high-frequency vocabulary. The knowledge point label is added to the basic knowledge point label collection.

When the target knowledge point is the target verb vocabulary, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain the sentence set, and processing the sentence set. The word set is obtained by the word segmentation process. Based on the word set, each word in the word set is marked with part of speech to obtain the set of part-of-speech tags. The word whose part of speech is the verb part of speech is used as the reference verb vocabulary, and the similarity value between the reference verb vocabulary and the target verb vocabulary is calculated. When the similarity value is greater than the similarity threshold, the basic knowledge point label associated with the target verb vocabulary is obtained, and the basic knowledge point label associated with the target verb vocabulary is added to the basic knowledge point label set.

When the target knowledge point is the target mathematical vocabulary, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain the sentence set, and processing the sentence set. The word set is obtained by the word segmentation process. Based on the word set, each word in the word set is marked with part of speech to obtain the set of part of speech marking, and the word whose part of speech is the part of speech of the numeral is used as the reference mathematical vocabulary, and the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary is calculated. When the similarity value is greater than the similarity threshold, the basic knowledge point labels associated with the target mathematical vocabulary are obtained, and the basic knowledge point labels associated with the target mathematical vocabulary are added to the basic knowledge point label set.

When the target knowledge point is the target phonetic symbol, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain a sentence set, and segmenting the sentence set. Process to obtain a word set, analyze each word in the word set, and label each word with a phonetic symbol to obtain a phonetic symbol set, calculate the similarity value between the phonetic symbol of the word in the phonetic symbol set and the target phonetic symbol, and obtain when the similarity value is greater than the similarity threshold. The basic knowledge point label associated with the target phonetic symbol is added to the basic knowledge point label set.

When the target knowledge point is the target sentence pattern, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain the sentence set, and processing the sentence set. The word set is obtained by the word segmentation process. Based on the word set, the part-of-speech tagging of each word in the word set is performed to obtain the part-of-speech tag set. The similarity value corresponding to the word, part of speech and syntax tree in the target sentence pattern, respectively, and when the similarity value is greater than the similarity threshold, the basic knowledge point label associated with the target sentence pattern is obtained, and the target sentence pattern The associated basic knowledge point label is added to the basic knowledge point label collection.

When the target knowledge point is the target grammar, the basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value, including: performing sentence segmentation on the text data to obtain a sentence set, and segmenting the sentence set. The word set is obtained by processing, and the part-of-speech tagging is performed on each word in the word set based on the word set to obtain the part-of-speech tagging set. When the degree threshold is reached, the basic knowledge point labels associated with the target grammar are obtained, and the basic knowledge point labels associated with the target grammar are added to the basic knowledge point label set.

S204: Generate a comprehensive knowledge point label set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point label set.

Among them, feature information refers to the type of raw data resources. For example, the types of raw data resources can include audio, video, text, picture books and other types. Different types can train/exercise the learning ability (such as listening, speaking, reading, writing, etc.) abilities) are different. The comprehensive knowledge point label in the comprehensive knowledge point label set refers to the knowledge point label generated based on the characteristic information of the original data resource and the basic knowledge point label set, which can reflect the ability of the original data resource to exercise the user (student), a kind of original data resource. There can be multiple corresponding comprehensive knowledge point labels, for example: the original data resource contains audio, and the basic knowledge point label set contains the knowledge point label associated with the target phonetic symbol, then the comprehensive knowledge point corresponding to the original data resource can be obtained by analysis The label is the hearing label.

Generally, the comprehensive knowledge point labels of the original data resources are related to the characteristics of the original data resources, and the comprehensive knowledge point labels in the comprehensive knowledge point label set and the basic knowledge point labels in the basic knowledge point label set are both associated with the original data resources. , after the basic knowledge point label set and the comprehensive knowledge point label set are generated, it means that the original data resource has been marked with the relevant knowledge point labels (including the basic knowledge point label and the comprehensive knowledge point label).

When the solution of the embodiment of the present application is executed, the server preprocesses the original data resource to obtain text data, and calculates the similarity between the text data and multiple target knowledge points to obtain a similarity value. According to the difference between the similarity value and the similarity threshold The comparison result generates the basic knowledge point label set of the original data resource, and generates the comprehensive knowledge point label set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point label set, which can quickly and accurately label the original data resource with Related knowledge point labels, improve the efficiency of labeling and the accuracy of labeling.

As described above, the embodiments of the present application are mainly described by taking the online education industry as an example, but those skilled in the art will understand that the application of this method is not limited to the online education industry, such as retail, transportation, social networking, search, education, The method described in this application can be applied to user label processing in various industries such as medical treatment.

Referring to FIG. 3, an embodiment of the present application provides a schematic flowchart of a method for labeling data resources. The method for labeling data resources may include the following steps:

S301 , preprocessing the original data resource to obtain text data.

Among them, the original data resources refer to data resources in the form of text, audio, video, etc., such as exercises, picture books, learning audio, learning videos and other teaching resources, and the original data resources include the corresponding learning levels and subjects. Text data refers to the data after unified conversion of original data resources of text, audio, video and other types into text type. The basic knowledge points in the text data may include one or more of reference content vocabulary, reference high frequency vocabulary, reference verb vocabulary, reference mathematical vocabulary, reference phonetic symbol, reference sentence pattern and reference grammar.

Generally, when the original data resource is of audio or video type, the original data resource may be converted into text data of a preset text type through an ASR (Automatic Speech Recognition, automatic speech recognition) technology. ASR technology is a technology that converts audio to text based on a list of key words, converts audio (or audio in video) content into speech features through spectrum, and matches the speech features with the entries in the key word list, and The best matching result obtained is used as the recognition result. When the original data resource is a text type but not a preset text type, the text type needs to be converted into a preset text type. Common text types are: txt., doc., hlp., wps., rtf., htm., pdf, etc. The default text types can be set to different text types according to actual needs.

S302: Query attribute information corresponding to the original data resource from a preset knowledge graph, and obtain multiple target knowledge points corresponding to the attribute information.

The original data resources are teaching resources, the attribute information is course information, and the course information refers to course-related information such as learning levels and learning subjects corresponding to the original data resources. The target knowledge point includes at least one of the target content vocabulary, target high-frequency vocabulary, target verb vocabulary, target mathematical vocabulary, target phonetic symbol, target sentence pattern and target grammar. It is a knowledge point that can be obtained from the knowledge map. Different learning levels Corresponding to different target knowledge points, multiple target knowledge points are each associated with a basic knowledge point label. The knowledge map contains the target knowledge points of different learning levels, different years, and different learning subjects. It is also understood as knowledge domain visualization or knowledge domain mapping map, which is a series of various graphical data showing the relationship between knowledge development process and structure. Use visualization technology to describe knowledge resources and their carriers, mine, analyze, construct, draw and display knowledge and their interrelationships.

Generally, before calculating the similarity between text data and multiple target knowledge points to obtain the similarity value, it is necessary to obtain the course information corresponding to the original data resource, and query the course information from the preset knowledge map to obtain the corresponding multi-level information. A target knowledge point, and the knowledge map contains target knowledge points of different learning levels, different years, and different learning subjects. The similarity calculation is performed on the text data and multiple target knowledge points respectively, that is, the similarity value between the basic knowledge point in the text data and the target knowledge point is calculated, and the basic knowledge point corresponding to the text data can be determined based on the similarity value. Label, that is, the basic knowledge point label corresponding to the original data resource. Depending on the original data resource, different course information corresponding to it can be obtained. Based on the course information, multiple target knowledge points corresponding to the original data resource can be queried from the preset knowledge graph, but the original data resource may not be completely Contains all target knowledge points queried from the preset knowledge graph. The original data resource may contain one or more target knowledge points queried from the preset knowledge graph, so it is necessary to analyze the basic knowledge in the original data resource. The similarity value between the point and the target knowledge point in the preset knowledge graph. Based on the similarity value, the target knowledge point contained in the original data resource can be determined, and then the basic knowledge point label that can be associated with the original data resource can be determined.

S303 , performing sentence segmentation processing on the text data to obtain a sentence set, and performing block processing on the sentence set respectively to obtain a word block set, and word segmentation processing to obtain a word set.

The sentence set refers to a set containing multiple sentences obtained after sentence segmentation processing is performed on the text data, and the sentences in the sentence set are one or more sentences obtained by dividing the text data according to the text content, line breaks, punctuation marks, etc. full sentence. The word block set refers to a set including a plurality of phrases (word blocks) obtained by phrase division of each sentence in the sentence set. The word set refers to a set including a plurality of words obtained by dividing each sentence in the sentence set by words.

S304, based on the keyword extraction TF-IDF algorithm, calculate the importance degree weight of each word block in the word block set.

Among them, the importance degree weight refers to the weight of the importance degree corresponding to each word block obtained by analyzing the word block based on the keyword extraction TF-IDF algorithm, that is, the TF-IDF value, and the weight value refers to the weight of each word block in the weighted average. The frequency of the number, also known as the weight or weight.

In general, TF-IDF (Term Frequency–Inverse Document Frequency) is a commonly used weighting technique for information retrieval and data mining, and TF-IDF is a statistical method used to evaluate words for a document set or a corpus. The importance of one of the documents, the importance of a word increases proportionally to the number of times it appears in the document, but at the same time decreases inversely proportional to the frequency of its occurrence in the corpus. TF stands for Term Frequency (TermFrequency), which means the number of occurrences of the corpus divided by the total number of sentences in the question-and-answer database, and IDF stands for weight, which means the inverse document frequency of word blocks. The main idea of IDF is: if there are fewer documents containing term t, that is, the smaller n is, and the larger the IDF is, it means that term t has a good ability to distinguish categories. If the number of documents containing term t in a certain type of document C is m, and the total number of documents containing t in other types is k, obviously the number of documents containing t is n=m+k. When m is large, n is also large. , the value of the IDF obtained according to the IDF formula will be small, which means that the classification ability of the entry t is not strong.

The calculation process of the TF-IDF algorithm based on keyword extraction mainly includes: calculating the word frequency, comparing the text content with different text lengths, and standardizing the "word frequency" of the word blocks; calculating the inverse document frequency, using the corpus (corpus) to simulate the use of language Environment, if a word occurs more frequently, the denominator will be larger, and the inverse document frequency will be smaller (closer to 0). Add 1 to the denominator to avoid the case where the denominator is 0 (that is, all documents do not contain the word). ; Calculate the TF-IDF value. The TF-IDF value is proportional to the number of occurrences of a word in the text and inversely proportional to the number of occurrences of the word in the entire language. After calculating the TF-IDF of each word in the text After the IDF value, it can be sorted in descending order according to the TF-IDF value.

S305 , using the word block with the importance degree weight greater than the first preset weight value as the reference content vocabulary, and using the word block with the importance degree weight value less than or equal to the second preset weight value as the reference high frequency vocabulary.

Wherein, the first preset value is a basis for filtering reference content words, and the first preset weight and the first preset weight can be set according to actual needs. Reference content vocabulary refers to words that contain a certain meaning. The second preset weight is a basis for screening and referring to high-frequency words, and usually the first preset weight is smaller than the second preset weight.

Generally, after the importance of each word block in the word block set is scored based on the keyword extraction TF-IDF algorithm, the word blocks of the importance degree weights can be sorted in descending order, and the first 1/3 of the word blocks sorted in descending order are selected. As reference content words, the last 1/3 word blocks in descending order are taken as reference high frequency words.

S306: Calculate the similarity value between the reference content word and the target content word, and the similarity value between the reference high frequency word and the target high frequency word.

Among them, the similarity value refers to the similarity relationship between the two quantities being compared. Generally, the larger the similarity value is, the more similar the two quantities are. Here, the similarity value can be the difference between the reference content vocabulary and the target content vocabulary. The similarity value and the similarity value between the reference high-frequency vocabulary and the target high-frequency vocabulary. Both the target content vocabulary and the target high-frequency vocabulary are the target knowledge points corresponding to the course information of the original data resource in the knowledge graph, and the reference content vocabulary and the reference high-frequency vocabulary are the basic knowledge points in the text data of the original data resource.

S307, when the similarity value is greater than the similarity threshold, acquire the basic knowledge point label associated with the target content vocabulary and the basic knowledge point label associated with the target high-frequency vocabulary.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the conditions, that is, the similarity threshold value. According to the different basic knowledge points, the corresponding similarity threshold value can also be different. The similarity threshold is different from the similarity threshold corresponding to the reference high-frequency vocabulary. The basic knowledge point label is associated with the target knowledge point in the knowledge graph. If the basic knowledge point of the original data resource contains a basic knowledge point whose similarity value is greater than the similarity threshold with the target knowledge point in the knowledge graph, the The basic knowledge point can be associated with the basic knowledge point label of the corresponding target knowledge point. Labels are data used to describe the characteristics of data resources. Different data resources correspond to different label data. Labels can effectively represent the knowledge points, learning content or learning capabilities involved in data resources, and labeling data resources with different labels can be used. Realize the screening and analysis of data resources.

For example: divide the sentence set {S1, S2...Sn} obtained by segmentation processing into blocks (Chunk) by sentence to obtain the word block set {B11,B12...B1m1},{B21,B22...}..., corresponding to each sentence, Since most of the reference content vocabulary is based on the vocabulary of the learning topic, in order to speed up the calculation, the TF-IDF algorithm based on keyword extraction is used to evaluate the importance of each word block set {B11,B12...B1m1},{B21,B22...}... Score the importance weights of the word blocks in each word block set. After sorting the importance weights in descending order, take the first 1/3 of the word blocks as the reference content vocabulary, that is, the importance weight is greater than the first preset weight. The word block of the value is used as the reference content word, and the similarity calculation is performed with the target content word in turn. The reference content word is recorded as Bi, and the target content word is recorded as Kj, and the edit distance similarity sim_raw1 between Bi and Kj is calculated respectively, and the part of speech is restored after editing. Distance acquaintance sim_lemma1 and semantic similarity sim_sem1, according to the above similarity to calculate the total similarity value voc_score1=vocα·sim_raw+vocβ··sim_lemma+vocγ·sim_sem, if the total similarity value is higher than the first preset similarity threshold voc_score_threshold1 , the reference content word Bi is marked with the knowledge point label associated with the target content word Kj.

For example: divide the sentence set {S1, S2...Sn} obtained by segmentation processing into blocks (Chunk) by sentence to obtain the word block set {B11,B12...B1m1},{B21,B22...}..., corresponding to each sentence, Since most of the reference content vocabulary is based on the vocabulary of the learning topic, in order to speed up the calculation, the TF-IDF algorithm based on keyword extraction is used to evaluate the importance of each word block set {B11,B12...B1m1},{B21,B22...}... Score the importance weights of the word blocks in each word block set. After sorting the importance weights in descending order, take the last 1/3 of the word blocks as the reference high-frequency vocabulary, and compare the similarity with the target high-frequency vocabulary in turn. Calculate, refer to high-frequency vocabulary as Ba, target high-frequency vocabulary as Kb, calculate the edit distance similarity sim_raw2 of Ba and Kb respectively, after the part-of-speech restoration, edit distance acquaintance sim_lemma2 and semantic similarity sim_sem2, calculated according to the above similarity The total similarity value voc_score2=vocα·sim_raw+vocβ·sim_lemma+vocγ·sim_sem, if the total similarity value is higher than the preset similarity threshold voc_score_threshold2, the knowledge points associated with the target high-frequency vocabulary Kb will be marked with reference to the high-frequency vocabulary Ba Label.

S308, adding the basic knowledge point label associated with the target content vocabulary and the basic knowledge point label associated with the target high-frequency vocabulary to the basic knowledge point label set.

Among them, the basic knowledge point label set is a collection of basic knowledge point labels corresponding to the original data resources, and the basic knowledge point labels included in the basic knowledge point label set are the basic knowledge point labels associated with the target knowledge points whose similarity value is greater than the similarity threshold. , according to the different basic knowledge points, the corresponding similarity thresholds are also different. The number of basic knowledge point tags in the basic knowledge point tag set is related to the content of the original data resources, and the basic knowledge point tags in the basic knowledge point tag set are associated with the original data resources, that is, the basic knowledge point tags in the basic knowledge point tag set. The basic knowledge point label is equivalent to the corresponding basic knowledge point label that has been marked on the original data resource.

S309, performing part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set.

The part-of-speech tagging set refers to a part-of-speech set corresponding to each word in the word set, and each word in the word set has a one-to-one mapping relationship with each part of speech in the part-of-speech tagging set. The parts of speech in the part-of-speech tagging set can include nouns, verbs, adjectives, numerals, quantifiers, pronouns, adverbs, prepositions, conjunctions, auxiliary words, interjections, onomatopoeia, etc. The specific parts of speech are still related to the learning content in the original data resources.

Generally, the part-of-speech tagging process usually needs to determine the most appropriate part-of-speech for each word according to the context of the sentence. There will also be multiple words in the part-of-speech tagging process, just as a word can be used as a noun or a verb. , also known as concurrent words, such words have a high probability of appearing in commonly used words. This situation can be solved by using a probability method, for example, HMM (Hidden Markov Model, Hidden Markov Model) can be used to process the labeling of such words. In addition, part-of-speech tagging can also be performed on words based on transformation thinking or classification thinking-based methods.

S310 , the words whose part of speech is the part of speech of the verb are used as the reference verb vocabulary, and the words whose part of speech is the part of speech of the numeral are used as the reference mathematical vocabulary.

Generally, the part of speech corresponding to the words contained in the original data resource can be obtained after part-of-speech analysis of the words in the word set, so words whose part-of-speech is verb part-of-speech and numeral part-of-speech can be screened out respectively according to the part-of-speech, and the part-of-speech as Words with verb parts of speech are used as reference verb vocabulary knowledge points, and words whose part of speech is numeral part of speech are used as reference mathematical vocabulary knowledge points.

S311, calculate the similarity value between the reference verb vocabulary and the target verb vocabulary, and the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary.

Among them, the reference verb vocabulary and the reference mathematical vocabulary are the basic knowledge points contained in the original data resource, and the target verb vocabulary and the target mathematical vocabulary are the target knowledge points corresponding to the course information of the original data resource in the knowledge map. The similarity value refers to the similarity between the two quantities being compared. Usually, the larger the similarity value, the more similar the two quantities are. Here, the similarity value can be the similarity between the reference verb vocabulary and the target verb vocabulary. value and the similarity value of the reference math vocabulary to the target math vocabulary.

S312, when the similarity value is greater than the similarity threshold, acquire the basic knowledge point label associated with the target verb vocabulary and the basic knowledge point label associated with the target math vocabulary.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the conditions, that is, the similarity threshold value. According to the different basic knowledge points, the corresponding similarity threshold value can also be different. The similarity threshold is different from the similarity threshold corresponding to the reference mathematical vocabulary.

S313: Add the basic knowledge point label associated with the target verb vocabulary and the basic knowledge point label associated with the target mathematical vocabulary to the basic knowledge point label set.

Among them, the basic knowledge point label is associated with the target knowledge point in the knowledge graph. If the basic knowledge point of the original data resource contains a basic knowledge point whose similarity value is greater than the similarity threshold value with the target knowledge point in the knowledge graph, Then, the basic knowledge point can be associated with the basic knowledge point label of the corresponding target knowledge point. Labels are data used to describe the characteristics of data resources. Different data resources correspond to different label data. Labels can effectively represent the knowledge points, learning content or learning capabilities involved in data resources, and labeling data resources with different labels can be used. Realize the screening and analysis of data resources.

For example: the sentence set {S1, S2...Sn} obtained by segmentation is processed by sentence segmentation to obtain the corresponding word set {{T11,T12...T1o1},{T21,T22...T2o2}...}, and the Each word in the word set is marked by part of speech to obtain a set of part-of-speech tags, and the words whose part of speech is the part of speech of the verb are used as the reference verb vocabulary, and the words whose part of speech is the part of speech of the numeral are used as the reference mathematical vocabulary; the reference verb vocabulary is similar to the target verb vocabulary in turn. Degree calculation, the reference verb vocabulary is recorded as Vi, the target verb vocabulary is recorded as Uj, the similarity between the numeral vocabulary and the target numeral vocabulary is calculated in turn, the numeral vocabulary is recorded as Va, the target numeral vocabulary is recorded as Ub, and Vi is calculated respectively. The edit distance similarity value sim_raw3 with Uj, the edit distance acquaintance degree sim_lemma3 and the semantic similarity degree sim_sem3 after the part-of-speech restoration, the total similarity value voc_score3=vocα·sim_raw+vocβ·sim_lemma+vocγ·sim_sem is calculated according to the above similarity. If the similarity value is higher than the third preset similarity threshold voc_score_threshold3, the knowledge point label associated with the target verb vocabulary Uj will be marked with the reference verb vocabulary Vi; and the edit distance similarity value sim_raw4 of Va and Ub will be calculated respectively, and edited after the part of speech is restored. The distance acquaintance degree sim_lemma4 and the semantic similarity degree sim_sem4, the total similarity value voc_score4=vocα·sim_raw+vocβ·sim_lemma+vocγ·sim_sem is calculated according to the above similarity, if the total similarity value is higher than the fourth preset similarity threshold voc_score_threshold4, Then, the numeral vocabulary Va is marked with the knowledge point label associated with the target numeral vocabulary Ub.

S314, analyze each word in the word set, and label each word with phonetic symbols to obtain a phonetic symbol set.

The phonetic symbol set is a set including phonetic symbols corresponding to each word in the word set, and each word in the word set has a one-to-one mapping relationship with each phonetic symbol in the phonetic symbol set.

S315: Calculate the similarity value between the word phonetic symbol in the phonetic symbol set and the target phonetic symbol.

Among them, the word phonetic symbols in the phonetic symbol collection are the basic knowledge points contained in the original data resources, and the target phonetic symbols are the target knowledge points in the knowledge map corresponding to the course information of the original data resources. The similarity value refers to the similarity relationship between the two quantities being compared. Generally, the larger the similarity value is, the more similar the two quantities are. Here, the similarity value may be the similarity value between the word phonetic symbol and the target phonetic symbol.

S316, when the similarity value is greater than the similarity threshold, acquire the basic knowledge point label associated with the target phonetic symbol.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the condition, that is, the similarity threshold value. According to the different basic knowledge points, the corresponding similarity threshold value can also be different. The degree threshold can be arbitrarily set as required, and may also be different from the aforementioned similarity threshold.

S317, add the basic knowledge point tag associated with the target phonetic symbol to the basic knowledge point tag set.

Among them, the basic knowledge point label is associated with the target knowledge point in the knowledge graph. If the basic knowledge point of the original data resource contains a basic knowledge point whose similarity value is greater than the similarity threshold value with the target knowledge point in the knowledge graph, Then, the basic knowledge point can be associated with the basic knowledge point label of the corresponding target knowledge point. Labels are data used to describe the characteristics of data resources. Different data resources correspond to different label data. Labels can effectively represent the knowledge points, learning content or learning capabilities involved in data resources, and labeling data resources with different labels can be used. Realize the screening and analysis of data resources.

For example: the sentence set {S1, S2...Sn} obtained by segmentation is processed by sentence segmentation to obtain the corresponding word set {{T11,T12...T1o1},{T21,T22...T2o2}...}, through The dictionary tool converts the words in the word set into the corresponding phonetic symbol set {{P11,P12…P1o1},{P21,P22…P2o2}…}, and sequentially compares the word phonetic symbol Pi in the phonetic symbol set with the target phonetic symbol Kj. Degree calculation, including whether the pronunciation combination contains similarity sim_in in the source word Ti corresponding to the word phonetic symbol Pi, the edit distance similarity sim_edit between Pi and Kj, and the longest common substring similarity sim_lcs between the word phonetic symbol Pi and the target phonetic symbol Kj , calculate the total similarity value phon_score=phonα·sim_in+phonβ·sim_edit+phonγ·sim_lcs according to the above scores, if the total similarity value is higher than the preset similarity threshold phon_score_threshold, then the word phonetic symbol Pi is marked with the knowledge associated with the target phonetic symbol Kj Click the label.

S318, perform dependency syntax analysis on the sentences in the sentence set to obtain a dependency syntax tree.

Among them, the dependency syntax tree refers to a relationship tree that can describe the dependency relationship between each word in the data resource, and can express the syntactic collocation relationship between each word, and this collocation relationship is related to semantics. The basic task of dependency syntax analysis is to determine the syntactic structure of a sentence or the dependencies between words in a sentence, which mainly includes two aspects: determining the grammatical system of the language, that is, giving a formal definition to the grammatical structure of legal sentences in the language; Syntactic analysis technology, that is, according to a given grammar system, automatically deduce the syntactic structure of a sentence, and analyze the relationship between the syntactic units and syntactic units contained in the sentence.

S319: Calculate the similarity values corresponding to the words in the word set, the part of speech and the dependent syntax tree in the part of speech tagging set and the words, part of speech and syntax tree in the target sentence pattern respectively.

The similarity value refers to the similarity between the two quantities being compared. Generally, the larger the similarity value is, the more similar the two quantities are. Here, the similarity value can be the words in the word set of the original data resource. , the similarity values of the parts of speech and dependent syntax trees in the part of speech tagging set respectively corresponding to the words, parts of speech and syntax trees in the target sentence pattern.

S320, when the similarity value is greater than the similarity threshold, acquire the basic knowledge point label associated with the target sentence pattern.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the condition, that is, the similarity threshold value. According to different basic knowledge points, the corresponding similarity threshold value can also be different. The similarity threshold corresponding to the part of speech and the dependent syntax tree in the part-of-speech tagging set can be arbitrarily set as required, and may also be different from the similarity threshold described above.

S321, adding the basic knowledge point label associated with the target sentence pattern to the basic knowledge point label set.

Among them, the basic knowledge point label is associated with the target knowledge point in the knowledge graph. If the basic knowledge point of the original data resource contains a basic knowledge point whose similarity value is greater than the similarity threshold value with the target knowledge point in the knowledge graph, Then, the basic knowledge point can be associated with the basic knowledge point label of the corresponding target knowledge point. Labels are data used to describe the characteristics of data resources. Different data resources correspond to different label data. Labels can effectively represent the knowledge points, learning content or learning capabilities involved in data resources, and labeling data resources with different labels can be used. Realize the screening and analysis of data resources.

For example: the sentence set {S1, S2...Sn} obtained by segmentation is processed by sentence segmentation to obtain the corresponding word set {{T11,T12...T1o1},{T21,T22...T2o2}...}, and the Sentence set {S1, S2…Sn} is processed by part-of-speech tagging and dependency syntax analysis, and the part-of-speech tagging set {{Pos11, Pos12…Pos1o1}, {Pos21, Pos22…Pos2o2}…} can be obtained respectively, and the dependency syntax tree {Tree1, Tree2...Treen}, and then calculate the jaccard similarity value sim_token_jaccard, sim_token_jaccard, sim_token_jaccard, Edit distance similarity sim_pos_edit, longest common substring similarity value sim_pos_lcs, treei and target sentence Formula knowledge point example tree similarity sim_tree of KTreej, calculate the total similarity value according to the above similarity value sent_score=sentα·sim_token_jaccard+sentβ·sim_pos_edit+sentγ·sim_pos_lcs+sentθ·sim_tree, if the total similarity value is higher than the preset similarity The threshold value sent_score_threshold, the sentence Si is marked with the basic knowledge point label associated with the Kj sentence pattern.

S322: Calculate, based on the word set and the part-of-speech tagging set, a similarity value between the grammar included in the sentence set and the target grammar.

The grammar contained in the sentence set is the basic knowledge point of the original data resource, and the target grammar is the target knowledge point corresponding to the course information of the original data resource in the knowledge graph. The similarity value refers to the similarity between the two quantities being compared. Usually, the larger the similarity value, the more similar the two quantities are. Here, the similarity value can be the grammar and target in the word set of the original data resource. The similarity value corresponding to the grammar.

S323, when the similarity value is greater than the similarity threshold, acquire the basic knowledge point label associated with the target grammar.

Among them, the similarity threshold refers to the minimum lower limit value that the similarity value can satisfy the condition, that is, the similarity threshold value. According to the different basic knowledge points, the corresponding similarity threshold value can also be different. The corresponding similarity threshold can be arbitrarily set as required, and may also be different from the similarity threshold described above.

S324, adding the basic knowledge point tag associated with the target grammar to the basic knowledge point tag set.

Among them, the basic knowledge point label is associated with the target knowledge point in the knowledge graph. If the basic knowledge point of the original data resource contains a basic knowledge point whose similarity value is greater than the similarity threshold value with the target knowledge point in the knowledge graph, Then, the basic knowledge point can be associated with the basic knowledge point label of the corresponding target knowledge point. Labels are data used to describe the characteristics of data resources. Different data resources correspond to different label data. Labels can effectively represent the knowledge points, learning content or learning capabilities involved in data resources, and labeling data resources with different labels can be used. Realize the screening and analysis of data resources.

For example: the sentence set {S1, S2...Sn} obtained by segmentation is processed by sentence segmentation to obtain the corresponding word set {{T11,T12...T1o1},{T21,T22...T2o2}...}, and the Sentence set {S1, S2…Sn} is processed by part-of-speech tagging, and part-of-speech tagging set {{Pos11,Pos12…Pos1o1},{Pos21,Pos22…Pos2o2}…} can be obtained, and whether the grammatical fragment is included in the word set {Ti1,Ti2 ...Tim}, Ti1={T11,T12...T1o1}, Ti2={T21,T22...T2o2}...or part-of-speech tagging set {KPosj1,KPosj2...KPosjn}, KPosj1={Pos11,Pos12...Pos1o1}, KPosj2={Pos21 ,Pos22...Pos2o2}... contains the similarity sim_in, the position similarity sim_position of the grammar fragment in the word Ti1={T11, T12...T1o1} and the target word {KTj1, KTj2...KTjn}, the part of speech similarity sim_pos of the grammar fragment , calculate the total similarity value gram=gramα·sim_in+gramβ·sim_position+gramγ·sim_pos according to the above similarity, if the total similarity value is higher than the preset similarity threshold gram_score_threshold, then mark the sentence Si on the target sentence Kj grammar associated The basics of point labels.

For example: Calculate the basic knowledge point label and quantity corresponding to the original data resource, which can be recorded as: Tag(Text)={voc:num1,verb:num2,math:num3,hfw:num4,phon:num5,sent: num5,gram:num7|numi>=0}. The entire process of calculating the similarity between the basic knowledge point of the original data resource and the target knowledge point corresponding to the course information of the original data resource in the knowledge graph is shown in FIG. 4 .

S325: Generate a comprehensive knowledge point label set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point label set.

Among them, feature information refers to the type of raw data resources. For example, the types of raw data resources can include audio, video, text, picture books and other types. Different types can train/exercise the learning ability (such as listening, speaking, reading, writing, etc.) abilities) are different. The comprehensive knowledge point label in the comprehensive knowledge point label set refers to the knowledge point label generated based on the characteristic information of the original data resource and the basic knowledge point label set, which can reflect the ability of the original data resource to exercise the user (student), a kind of original data resource. There can be multiple corresponding comprehensive knowledge point labels, for example: the original data resource contains audio, and the basic knowledge point label set contains the knowledge point label associated with the target phonetic symbol, then the comprehensive knowledge point corresponding to the original data resource can be obtained by analysis The label is the hearing label.

Generally, the comprehensive knowledge point labels of the original data resources are related to the characteristics of the original data resources, and the comprehensive knowledge point labels in the comprehensive knowledge point label set and the basic knowledge point labels in the basic knowledge point label set are both associated with the original data resources. , after the basic knowledge point label set and the comprehensive knowledge point label set are generated, it means that the original data resource has been marked with the relevant knowledge point labels (including the basic knowledge point label and the comprehensive knowledge point label).

When the solutions of the embodiments of the present application are executed, the server preprocesses the original data resources to obtain text data, queries the attribute information corresponding to the original data resources from the preset knowledge graph, and obtains multiple target knowledge points corresponding to the attribute information. The text data is processed by sentence segmentation to obtain a sentence set, and the sentence set is processed into blocks to obtain a word block set. Based on the keyword extraction TF-IDF algorithm, the importance degree weight of each word block in the word block set is calculated, and the importance degree weight is calculated. The word block whose value is greater than the first preset weight is used as the reference content vocabulary, the word block whose importance degree weight is less than or equal to the second preset weight is used as the reference high-frequency vocabulary, the reference content vocabulary and the target content vocabulary are calculated, and the reference content vocabulary is calculated. The similarity value between the high-frequency vocabulary and the target high-frequency vocabulary, when the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target content vocabulary and the basic knowledge point label associated with the target high-frequency vocabulary, and associate the target content vocabulary The basic knowledge point label and the basic knowledge point label associated with the target high-frequency vocabulary are added to the basic knowledge point label set. Based on the word set, each word in the word set is part-of-speech tagging to obtain the part-of-speech tagging set, and the part-of-speech is the word of the verb part-of-speech. As the reference verb vocabulary, take the words whose part of speech is the numeral part as the reference mathematical vocabulary, calculate the similarity value between the reference verb vocabulary and the target verb vocabulary, and the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary, if the similarity value is greater than the similarity At the threshold, the basic knowledge point label associated with the target verb vocabulary and the basic knowledge point label associated with the target mathematical vocabulary are obtained, and the basic knowledge point label associated with the target verb vocabulary and the basic knowledge point label associated with the target mathematical vocabulary are added to the basic knowledge point. In the label set, analyze each word in the word set, and label each word with a phonetic symbol to obtain a phonetic symbol set, calculate the similarity value between the phonetic symbol of the word in the phonetic symbol set and the target phonetic symbol, and obtain the target when the similarity value is greater than the similarity threshold. Basic knowledge point tags associated with phonetic symbols, add the basic knowledge point tags associated with the target phonetic symbols to the basic knowledge point tag set, perform dependency syntax analysis on the sentences in the sentence collection to obtain the dependency syntax tree, and calculate the words and part-of-speech tags in the word collection. The similarity value corresponding to the word, part of speech and syntax tree in the set is respectively corresponding to the word, part of speech and syntax tree in the target sentence pattern. When the similarity value is greater than the similarity threshold, the basic knowledge point label associated with the target sentence pattern is obtained, and the target sentence pattern is associated with the similarity value. The basic knowledge point label associated with the sentence pattern is added to the basic knowledge point label set, and the similarity value between the grammar contained in the sentence set and the target grammar is calculated based on the word set and part-of-speech label set. When the similarity value is greater than the similarity threshold, the target is obtained. The basic knowledge point label associated with the grammar, the basic knowledge point label associated with the target grammar is added to the basic knowledge point label set, and the comprehensive knowledge point label set of the original data resource is generated according to the feature information of the original data resource and the basic knowledge point label set. In this way, knowledge point labels related to the original data resources can be quickly and accurately labeled, and the labeling efficiency and labeling accuracy can be improved.

As described above, the embodiment is mainly described by taking the online education industry as an example, but those skilled in the art will understand that the application of this method is not limited to the online education industry, such as retail, transportation, social networking, search, education, medical treatment, etc. The method described in this application can be applied to the processing of user labels in various industries.

The following are apparatus embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

Please refer to FIG. 5 , which shows a schematic structural diagram of an apparatus for labeling data resources provided by an exemplary embodiment of the present application. The device 5 is hereinafter referred to as the device 5, and the device 5 can be implemented as all or a part of the terminal through software, hardware or a combination of the two. The apparatus 5 includes a preprocessing module 501 , a calculation module 502 , a first processing module 503 , and a second processing module 504 .

A preprocessing module 501, configured to preprocess the original data resource to obtain text data;

The calculation module 502 is used to calculate the similarity value between the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein, each of the plurality of target knowledge points is associated with a basic knowledge point label;

The first processing module 503 is configured to generate the basic knowledge point label set of the original data resource according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: similar The basic knowledge point label associated with the target knowledge point whose degree value is greater than the similarity threshold;

The second processing module 504 is configured to generate a comprehensive knowledge point tag set of the original data resource according to the feature information of the original data resource and the basic knowledge point tag set.

Optionally, the device 5 also includes:

a query unit, configured to query the attribute information corresponding to the original data resource from the preset knowledge graph, and obtain the multiple target knowledge points corresponding to the attribute information

Optionally, the original data resources in the device 5 are teaching resources, and the attribute information is course information.

Optionally, the first processing module 503 includes:

a first processing unit, configured to perform sentence segmentation processing on the text data to obtain a sentence set, perform block processing on the sentence set respectively to obtain a word block set, and perform word segmentation processing to obtain a word set;

The first calculation unit is configured to analyze the word block set and the word set to obtain a reference vocabulary set, and calculate the similarity value of each reference vocabulary in the reference vocabulary set and the corresponding target knowledge points; The reference vocabulary set includes reference content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, and the target knowledge point corresponding to the reference high-frequency vocabulary is Target high-frequency vocabulary, the target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary;

The first adding unit is used to add the basic knowledge point label corresponding to the corresponding target knowledge point to the basic knowledge point when the similarity value of the corresponding target knowledge point is greater than the similarity threshold value. in the knowledge point label set; or

a second processing unit, configured to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

The second computing unit is configured to analyze the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculate the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set, and the sentence set in the sentence set. The similarity value of the grammar and the corresponding target knowledge point; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge corresponding to the grammar dot is the target grammar;

The second adding unit is configured to add the basic knowledge point label corresponding to the corresponding target knowledge point to the basic knowledge point when the similarity value of the corresponding target knowledge point is greater than the corresponding similarity threshold value. Knowledge point label collection.

Optionally, the first processing module 503 includes:

a third processing unit, configured to perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set;

The third computing unit is used to calculate the importance degree weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm;

a first selection unit, configured to use the word block whose importance weight is greater than the first preset weight as the reference content word;

a fourth calculation unit, configured to calculate the similarity value between the reference content vocabulary and the target content vocabulary;

a first obtaining unit, configured to obtain the basic knowledge point label associated with the target content vocabulary when the similarity value is greater than the similarity threshold;

The first adding unit is configured to add the basic knowledge point tag associated with the target content vocabulary to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

a fourth processing unit, configured to perform sentence segmentation processing on the text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set;

The fourth computing unit is used to calculate the importance degree weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm;

a second selection unit, configured to use the word block whose importance weight is less than or equal to the second preset weight as the reference high-frequency word;

a fifth calculation unit, used for calculating the similarity value between the reference high-frequency vocabulary and the target high-frequency vocabulary;

a second obtaining unit, configured to obtain the basic knowledge point label associated with the target high-frequency vocabulary when the similarity value is greater than the similarity threshold;

The second adding unit is configured to add the basic knowledge point tags associated with the target high-frequency vocabulary to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

a fifth processing unit, configured to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

a first tagging unit, configured to perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set;

The third selection unit is used to use the word whose part of speech is the part of speech of the verb as the reference verb vocabulary;

a sixth calculating unit, configured to calculate the similarity value between the reference verb vocabulary and the target verb vocabulary;

a third obtaining unit, configured to obtain the basic knowledge point label associated with the target verb vocabulary when the similarity value is greater than the similarity threshold;

The third adding unit is configured to add the basic knowledge point tag associated with the target verb vocabulary to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

The sixth processing unit is used to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

a second tagging unit, configured to perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set;

The fourth selection unit is used to use the words whose part of speech is a numeral part as a reference mathematical vocabulary;

A seventh calculation unit, used for calculating the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary;

a fourth obtaining unit, configured to obtain the basic knowledge point label associated with the target mathematical vocabulary when the similarity value is greater than the similarity threshold;

The fourth adding unit is configured to add the basic knowledge point tag associated with the target mathematical vocabulary to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

The seventh processing unit is used to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

The third labeling unit is used to analyze each word in the word set, and mark the word with phonetic symbols to obtain a phonetic symbol set;

The eighth computing unit, for calculating the similarity value of the word phonetic symbol in the phonetic symbol set and the target phonetic symbol;

The fifth selection unit is used to obtain the basic knowledge point label associated with the target phonetic symbol when the similarity value is greater than the similarity threshold;

The fifth adding unit is used for adding the basic knowledge point tag associated with the target phonetic symbol to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

The eighth processing unit is used to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

a fourth tagging unit, configured to perform part-of-speech tagging on each word in the word set based on the word set to obtain a part-of-speech tagging set;

an analysis unit, configured to perform dependency syntax analysis on the sentences in the sentence set to obtain a dependency syntax tree;

A ninth calculation unit, used to calculate the similarity values corresponding to the words in the word set, the parts of speech in the part-of-speech tagging set, and the dependent syntax tree respectively and the words, part of speech and syntax tree in the target sentence pattern;

a fifth obtaining unit, configured to obtain the basic knowledge point label associated with the target sentence pattern when the similarity value is greater than the similarity threshold;

The sixth adding unit is configured to add the basic knowledge point tag associated with the target sentence pattern to the basic knowledge point tag set.

Optionally, the first processing module 503 includes:

A ninth processing unit, configured to perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

a fifth tagging unit, configured to perform part-of-speech tagging on each word in the word set based on the word set to obtain a part-of-speech tagging set;

A tenth calculation unit, configured to calculate a similarity value between the grammar included in the sentence set and the target grammar based on the word set and the part-of-speech tag set;

a sixth obtaining unit, configured to obtain the basic knowledge point label associated with the target grammar when the similarity value is greater than the similarity threshold;

A seventh adding unit, configured to add the basic knowledge point tag associated with the target grammar to the basic knowledge point tag set.

It should be noted that, when the apparatus 5 provided in the above embodiment executes the data resource labeling method, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions may be allocated to different functional modules as required. To complete, that is, to divide the internal structure of the device into different functional modules to complete all or part of the functions described above. In addition, the method embodiments for labeling data resources provided by the above embodiments belong to the same concept, and details of the implementation process thereof are described in the method embodiments, which will not be repeated here.

As described above, the embodiment is mainly described by taking the online education industry as an example, but those skilled in the art will understand that the application of this method is not limited to the online education industry, such as retail, transportation, social networking, search, education, medical treatment, etc. The method described in this application can be applied to the processing of user labels in various industries.

FIG. 6 is a schematic structural diagram of an apparatus for labeling data resources provided by an embodiment of the present application, hereinafter referred to as apparatus 6, and apparatus 6 may be integrated into the aforementioned server or terminal equipment. As shown in FIG. 6, the apparatus includes: a memory 602, a processing 601, an input device 603, an output device 604 and a communication interface.

The memory 602 may be an independent physical unit, and may be connected to the processor 601, the input device 603 and the output device 604 through a bus. The memory 602, the processor 601, the input device 603, and the output device 604 can also be integrated together, implemented by hardware, or the like.

The memory 602 is used to store a program for implementing the above method embodiments or each module of the apparatus embodiment, and the processor 601 invokes the program to execute the operations of the above method embodiments.

The input device 602 includes but is not limited to a keyboard, a mouse, a touch panel, a camera and a microphone; the output device includes but is not limited to a display screen.

The communication interface is used to send and receive various types of messages, and the communication interface includes but is not limited to a wireless interface or a wired interface.

Optionally, when part or all of the distributed task scheduling method in the foregoing embodiment is implemented by software, the apparatus may also only include a processor. The memory for storing the program is located outside the device, and the processor is connected to the memory through a circuit/wire for reading and executing the program stored in the memory.

The processor may be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

The processor may further include a hardware chip. The above hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

The memory may include volatile memory (volatile memory), such as access memory (random-access memory, RAM); the memory may also include non-volatile memory (non-volatile memory), such as flash memory (flash memory), A hard disk drive (HDD) or a solid-state drive (SSD); the memory may also include a combination of the above types of memory.

Wherein, the processor 601 invokes the program code in the memory 602 to perform the following steps:

Preprocess raw data resources to obtain text data;

Perform similarity value calculation with the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein, each of the plurality of target knowledge points is associated with a basic knowledge point label;

The basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: the target whose similarity value is greater than the similarity threshold value Basic knowledge point labels associated with knowledge points;

A comprehensive knowledge point tag set of the original data resource is generated according to the feature information of the original data resource and the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

The attribute information corresponding to the original data resource is queried from a preset knowledge graph, and the multiple target knowledge points corresponding to the attribute information are obtained.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, perform block processing on the sentence set respectively to obtain a word block set, and perform word segmentation processing to obtain a word set;

Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary;

When the similarity values of the respective corresponding target knowledge points are greater than the respective corresponding similarity thresholds, add the basic knowledge point labels corresponding to the respective corresponding target knowledge points to the basic knowledge point label set; or

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set respectively to obtain a word set;

Analyzing the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculating the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set and the grammar in the sentence set and their corresponding targets The similarity value of knowledge points; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge point corresponding to the grammar is the target grammar;

When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set;

Calculate the importance weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm;

using the word block whose importance weight is greater than the first preset weight as the reference content word;

calculating the similarity value between the reference content vocabulary and the target content vocabulary;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target content vocabulary;

The basic knowledge point tags associated with the target content vocabulary are added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set;

Calculate the importance weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm;

using the word block whose importance weight is less than or equal to the second preset weight as the reference high-frequency word;

Calculate the similarity value between the reference high-frequency vocabulary and the target high-frequency vocabulary;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target high-frequency vocabulary;

The basic knowledge point tags associated with the target high-frequency vocabulary are added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set;

Use the words whose part of speech is the part of speech as the reference verb vocabulary;

calculating the similarity value between the reference verb vocabulary and the target verb vocabulary;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target verb vocabulary;

The basic knowledge point tags associated with the target verb vocabulary are added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set;

Use the words whose part of speech is a numeral part as a reference mathematical vocabulary;

Calculate the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target mathematical vocabulary;

The basic knowledge point tags associated with the target mathematical vocabulary are added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

Analyzing each word in the word set, and marking the word with phonetic symbols to obtain a phonetic symbol set;

Calculate the similarity value of the word phonetic symbol in the phonetic symbol set and the target phonetic symbol;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target phonetic symbol;

The basic knowledge point tag associated with the target phonetic symbol is added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

Perform part-of-speech tagging on each word in the word set based on the word set to obtain a part-of-speech tagging set;

Performing dependency syntax analysis on the sentences in the sentence set to obtain a dependency syntax tree;

Calculate the similarity values corresponding to the words in the word set, the parts of speech in the part-of-speech tagging set, and the dependent syntax tree respectively corresponding to the words, part of speech and syntax tree in the target sentence pattern;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target sentence pattern;

The basic knowledge point tags associated with the target sentence pattern are added to the basic knowledge point tag set.

In one or more embodiments, the processor 601 is further configured to:

Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set;

Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set;

Calculate, based on the word set and the part-of-speech tagging set, a similarity value between the grammar contained in the sentence set and the target grammar;

When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target grammar;

The basic knowledge point tags associated with the target grammar are added to the basic knowledge point tag set.

It should be noted that, when the apparatus 6 provided in the above embodiment executes the data resource labeling method, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions may be allocated to different functional modules as required. To complete, that is, to divide the internal structure of the device into different functional modules to complete all or part of the functions described above. In addition, the method embodiments for labeling data resources provided by the above embodiments belong to the same concept, and details of the implementation process thereof are described in the method embodiments, which will not be repeated here.

As described above, the embodiment is mainly described by taking the online education industry as an example, but those skilled in the art will understand that the application of this method is not limited to the online education industry, such as retail, transportation, social networking, search, education, medical treatment, etc. The method described in this application can be applied to the processing of user labels in various industries.

The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

Embodiments of the present application further provide a computer storage medium, where the computer storage medium can store multiple instructions, and the instructions are suitable for being loaded by a processor and executing the method steps of the embodiments shown in FIG. 2 to FIG. 3 above. , and the specific execution process may refer to the specific description of the embodiments shown in FIG. 2 to FIG. 3 , which will not be repeated here.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Claims (14)

1. a method for labeling data resources, wherein the method comprises: Preprocess raw data resources to obtain text data; Perform similarity calculation with the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein, each of the plurality of target knowledge points is associated with a basic knowledge point label; The basic knowledge point label set of the original data resource is generated according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: the target whose similarity value is greater than the similarity threshold value Basic knowledge point labels associated with knowledge points; A comprehensive knowledge point tag set of the original data resource is generated according to the feature information of the original data resource and the basic knowledge point tag set. 2. The method according to claim 1, wherein the method for determining the multiple target knowledge points comprises: The attribute information corresponding to the original data resource is queried from a preset knowledge graph, and the multiple target knowledge points corresponding to the attribute information are obtained. 3 . The method according to claim 2 , wherein the original data resources are teaching resources, and the attribute information is course information. 4 . 4. The method according to claim 1, wherein, generating the basic knowledge point label set of the original data resource according to the comparison result of the similarity value and the similarity threshold, comprising: Perform sentence segmentation processing on the text data to obtain a sentence set, perform block processing on the sentence set respectively to obtain a word block set, and perform word segmentation processing to obtain a word set; Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary; When the similarity values of the respective corresponding target knowledge points are greater than the respective corresponding similarity thresholds, add the basic knowledge point labels corresponding to the respective corresponding target knowledge points to the basic knowledge point label set; or Perform sentence segmentation processing on the text data to obtain a sentence set, and perform word segmentation processing on the sentence set respectively to obtain a word set; Analyzing the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculating the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set and the grammar in the sentence set and their corresponding targets The similarity value of knowledge points; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge point corresponding to the grammar is the target grammar; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set. 5. The method according to claim 4, wherein the target knowledge point comprises: target content vocabulary; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, performing block processing on the sentence set respectively to obtain a word block set, and performing word segmentation processing to obtain a word set; Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set; Calculate the importance weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm; using the word block whose importance weight is greater than the first preset weight as the reference content word; calculating the similarity value between the reference content vocabulary and the target content vocabulary; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target content vocabulary; The basic knowledge point tags associated with the target content vocabulary are added to the basic knowledge point tag set. 6. The method according to claim 4, wherein the target knowledge point comprises: target high-frequency vocabulary; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, performing block processing on the sentence set respectively to obtain a word block set, and performing word segmentation processing to obtain a word set; Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform block processing on the sentence set to obtain a word block set; Calculate the importance weight of each word block in the word block set based on the keyword extraction TF-IDF algorithm; using the word block whose importance weight is less than or equal to the second preset weight as the reference high-frequency word; Calculate the similarity value between the reference high-frequency vocabulary and the target high-frequency vocabulary; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target high-frequency vocabulary; The basic knowledge point tags associated with the target high-frequency vocabulary are added to the basic knowledge point tag set. 7. The method according to claim 4, wherein the target knowledge point comprises: target verb vocabulary; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, performing block processing on the sentence set respectively to obtain a word block set, and performing word segmentation processing to obtain a word set; Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set; Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set; Use the words whose part of speech is the part of speech as the reference verb vocabulary; calculating the similarity value between the reference verb vocabulary and the target verb vocabulary; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target verb vocabulary; The basic knowledge point tags associated with the target verb vocabulary are added to the basic knowledge point tag set. 8. The method according to claim 4, wherein the target knowledge point comprises: target mathematical vocabulary; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, performing block processing on the sentence set respectively to obtain a word block set, and performing word segmentation processing to obtain a word set; Analyzing the word block set and the word set to obtain a reference word set, and calculating the similarity value between each reference word in the reference word set and the corresponding target knowledge point; wherein, the reference word set includes reference Content vocabulary, reference high-frequency vocabulary, reference verb vocabulary and reference mathematical vocabulary, the target knowledge point corresponding to the reference content vocabulary is the target content vocabulary, the target knowledge point corresponding to the reference high-frequency vocabulary is the target high-frequency vocabulary, and the The target knowledge point corresponding to the reference verb vocabulary is the target verb vocabulary, and the target knowledge point corresponding to the reference mathematical vocabulary is the target mathematical vocabulary; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set; Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set; Use the words whose part of speech is a numeral part as a reference mathematical vocabulary; Calculate the similarity value between the reference mathematical vocabulary and the target mathematical vocabulary; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target mathematical vocabulary; The basic knowledge point tags associated with the target mathematical vocabulary are added to the basic knowledge point tag set. 9. method according to claim 4, is characterized in that, described target knowledge point comprises: target phonetic symbol; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, and performing word segmentation processing on the sentence set respectively to obtain a word set; Analyzing the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculating the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set and the grammar in the sentence set and their corresponding targets The similarity value of knowledge points; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge point corresponding to the grammar is the target grammar; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set; Analyzing each word in the set of words, and marking each word with phonetic symbols to obtain a set of phonetic symbols; Calculate the similarity value of the word phonetic symbol in the phonetic symbol set and the target phonetic symbol; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target phonetic symbol; The basic knowledge point tag associated with the target phonetic symbol is added to the basic knowledge point tag set. 10. The method according to claim 4, wherein the target knowledge point comprises: a target sentence pattern; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, and performing word segmentation processing on the sentence set respectively to obtain a word set; Analyzing the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculating the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set and the grammar in the sentence set and their corresponding targets The similarity value of knowledge points; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge point corresponding to the grammar is the target grammar; When the similarity values of the respective corresponding target knowledge points are greater than the respective similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set; Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set; Performing dependency syntax analysis on the sentences in the sentence set to obtain a dependency syntax tree; Calculate the similarity values corresponding to the words in the word set, the parts of speech in the part-of-speech tagging set, and the dependent syntax tree respectively corresponding to the words, part of speech and syntax tree in the target sentence pattern; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target sentence pattern; The basic knowledge point tags associated with the target sentence pattern are added to the basic knowledge point tag set. 11. The method according to claim 4, wherein the target knowledge point comprises: target grammar; Wherein, performing sentence segmentation processing on the text data to obtain a sentence set, and performing word segmentation processing on the sentence set respectively to obtain a word set; Analyzing the word set to obtain a phonetic symbol set, a part-of-speech tagging set and a dependency syntax tree respectively, and calculating the word phonetic symbol in the phonetic symbol set, the sentence pattern in the sentence set and the grammar in the sentence set and their corresponding targets The similarity value of knowledge points; wherein, the target knowledge point corresponding to the word phonetic symbol is the target phonetic symbol, the target knowledge point corresponding to the sentence pattern is the target sentence pattern, and the target knowledge point corresponding to the grammar is the target grammar; When the similarity values of the respective corresponding target knowledge points are greater than the respective corresponding similarity thresholds, the basic knowledge point labels corresponding to the respective corresponding target knowledge points are added to the basic knowledge point label set, including : Perform sentence segmentation processing on the learning text data to obtain a sentence set, and perform word segmentation processing on the sentence set to obtain a word set; Perform part-of-speech tagging on each word in the word set to obtain a part-of-speech tagging set; Calculate, based on the word set and the part-of-speech tagging set, a similarity value between the grammar contained in the sentence set and the target grammar; When the similarity value is greater than the similarity threshold, obtain the basic knowledge point label associated with the target grammar; The basic knowledge point tags associated with the target grammar are added to the basic knowledge point tag set. 12. A device for labeling data resources, wherein the device comprises: The preprocessing module is used to preprocess the original data resources to obtain text data; a calculation module, configured to calculate the similarity between the text data and a plurality of target knowledge points respectively to obtain a similarity value; wherein each of the plurality of target knowledge points is associated with a basic knowledge point label; The first processing module is configured to generate the basic knowledge point label set of the original data resource according to the comparison result of the similarity value and the similarity threshold value; wherein, the basic knowledge point label included in the basic knowledge point label set is: similarity The basic knowledge point label associated with the target knowledge point whose value is greater than the similarity threshold; The second processing module is configured to generate a comprehensive knowledge point tag set of the original data resource according to the characteristic information of the original data resource and the basic knowledge point tag set. 13. A computer storage medium, characterized in that the computer storage medium stores a plurality of instructions, the instructions are suitable for being loaded by a processor and performing the method steps of any one of claims 1-11. 14. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is adapted to be loaded by the processor and execute any one of claims 1 to 11 method steps for the item.

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