Method for processing skill information, model training method and device

Abstract

This disclosure provides a method, model training method, and device for processing skill information, relating to the field of artificial intelligence technology, specifically machine learning technology. A specific implementation scheme comprises: obtaining information to be examined; generating a model based on the information to be examined and pre-trained skill terms to determine at least one skill term; and outputting the at least one skill term. This implementation method allows skill information to be examined based on skill terms, improving the accuracy of skill examinations.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically the field of machine learning technology. Background Art

[0002] Currently, for different positions, it is necessary to examine the skill information corresponding to each position in order to select candidates suitable for each position.

[0003] However, the current assessment of skill information relies on manual experience, which makes the skill assessment more subjective and less accurate. Summary of the Invention

[0004] The present disclosure provides a method for processing skill information, a model training method, and an apparatus.

[0005] According to one aspect of the present disclosure, a method for processing skill information is provided, comprising: obtaining information to be examined; determining at least one skill word based on the information to be examined and a pre-trained skill word generation model; and outputting at least one skill word.

[0006] According to another aspect of the present disclosure, a model training method is provided, including: obtaining sample information to be examined and examination result annotation data; determining at least one sample skill word based on a preset skill word graph, sample information to be examined and a model to be trained; training the model to be trained based on at least one sample skill word and examination result annotation data to obtain a trained skill word generation model.

[0007] According to another aspect of the present disclosure, a device for processing skill information is provided, including: an information acquisition unit, configured to acquire information to be examined; a skill word determination unit, configured to determine at least one skill word based on the information to be examined and a pre-trained skill word generation model; and a skill word output unit, configured to output at least one skill word.

[0008] According to another aspect of the present disclosure, a model training device is provided, including: a sample acquisition unit, configured to acquire sample information to be examined and examination result annotation data; a sample skill word determination unit, configured to determine at least one sample skill word based on a preset skill word graph, sample information to be examined and a model to be trained; a model training unit, configured to train a model to be trained based on at least one sample skill word and examination result annotation data to obtain a trained skill word generation model.

[0009] According to another aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above methods for processing skill information or model training methods.

[0010] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any one of the above methods for processing skill information or model training methods.

[0011] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements any of the above methods for processing skill information or model training methods.

[0012] According to the technology disclosed herein, a method for processing skill information is provided, which can examine skill information based on skill words, thereby improving the accuracy of skill examination.

[0013] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0015] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0016] Figure 2 is a flow chart of one embodiment of a method for processing skill information according to the present disclosure;

[0017] Figure 3 is a schematic diagram of an application scenario of the method for processing skill information according to the present disclosure;

[0018] Figure 4 is a flowchart of an embodiment of a model training method according to the present disclosure;

[0019] Figure 5 is a flowchart of another embodiment of the model training method according to the present disclosure;

[0020] Figure 6 is a structural diagram of an embodiment of an apparatus for processing skill information according to the present disclosure;

[0021] Figure 7is a structural diagram of an embodiment of a model training device according to the present disclosure;

[0022] Figure 8 It is a block diagram of an electronic device used to implement the method for processing skill information or the model training method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0026] Terminal devices 101, 102, and 103 interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, and 103 can obtain information about candidates requiring assessment and positions awaiting assessment, and send this information to server 105 via network 104. Server 105 then responds with at least one corresponding skill term and outputs at least one skill term, allowing the user to conduct a skill assessment based on the at least one skill term. Furthermore, during the model training phase, terminal devices 101, 102, and 103 can obtain sample information awaiting assessment and labeled assessment results, and send this information to server 105 via network 104. Server 105 then performs model training based on this data and returns a trained skill term generation model.

[0027] Terminal devices 101, 102, and 103 can be hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices, including but not limited to mobile phones, computers, tablets, etc. When terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, to provide distributed services), or as a single software or software module. No specific limitation is made here.

[0028] The server 105 may be a server that provides various services. For example, the server 105 may obtain the information to be examined sent by the terminal devices 101, 102, and 103, determine at least one label word corresponding to the information to be examined based on a pre-trained skill word generation model, and return the at least one label word to the terminal devices 101, 102, and 103 via the network 104. Alternatively, during the model training phase, the server 105 may also receive sample information to be examined and labeled data of examination results sent by the terminal devices 101, 102, and 103, and train the model to be trained based on a preset skill word graph, sample information to be examined, and labeled data of examination results to obtain a trained skill word generation model.

[0029] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 105 is software, it can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitations are given here.

[0030] It should be noted that the method for processing skill information or the model training method provided in the embodiments of the present disclosure can be executed by the terminal devices 101, 102, 103, or by the server 105. The device for processing skill information or the model training device can be set in the terminal devices 101, 102, 103, or in the server 105.

[0031] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0032] Continue to refer Figure 2 , shows a process 200 of an embodiment of a method for processing skill information according to the present disclosure. The method for processing skill information of this embodiment includes the following steps:

[0033] Step 201: Obtain information to be examined.

[0034] In this embodiment, the execution subject (such as Figure 1 When a skills assessment is required, the terminal devices 101, 102, 103 or the server 105 can obtain information related to the skills assessment. The information may include, but is not limited to, information about the position to be assessed, information about the subject to be assessed, and historical assessment results of the subject to be assessed, etc., which is not limited in this embodiment. Furthermore, the execution entity can obtain the information from pre-stored local data or through a pre-established electronic device.

[0035] In some optional implementations of this embodiment, the information to be examined includes information about the position to be examined and information about the candidate to be examined. The information about the position to be examined is information about the position to be examined, such as job requirements. The information about the candidate to be examined is information about the candidate to be examined, such as a resume of the candidate.

[0036] Step 202: Determine at least one skill word based on the information to be examined and a pre-trained skill word generation model.

[0037] In this embodiment, the pre-trained skill word generation model is used to determine at least one skill word that matches the information to be examined, so that the recruiter can conduct a skill examination on the candidate based on the at least one skill word.

[0038] Among them, the pre-trained skill word generation model can be determined based on historical inspection data and the skill word graph corresponding to the historical inspection data. The historical inspection data can include historical information to be inspected and the inspection results corresponding to the historical information to be inspected.

[0039] Step 203: output at least one skill word.

[0040] In this embodiment, the execution entity may output at least one skill word so that the recruitment entity can conduct a recruitment assessment on the candidate based on the at least one skill word. Optionally, the execution entity may output at least one skill word to the bound electronic device of the recruitment entity.

[0041] In some optional implementations of this embodiment, outputting at least one skill word includes: determining a target recruitment object based on the at least one skill word and recruitment object information; sending at least one skill word to the target recruitment object so that the target recruitment object conducts a skill assessment on the candidate based on the at least one skill word.

[0042] In this implementation, after determining that at least one skill word has been obtained, the executing entity can obtain recruitment target information. The recruitment target information can include object information corresponding to each recruitment target. Optionally, the object information can include object tags. The executing entity can match the at least one skill word with the object information of each recruitment target, determine the degree of match between each recruitment target and the at least one skill word, and select the recruitment target with the highest degree of match as the target recruitment target. The executing entity can then send the at least one skill word to the electronic device bound to the target recruitment target, so that the target recruitment target can conduct a skills assessment on the candidate to be assessed based on the at least one skill word.

[0043] Furthermore, for each skill word, the execution entity can determine the target candidate with the highest matching degree for that skill word based on the skill word and the candidate's information. The target candidate can then be sent the skill word, allowing the target candidate to assess the candidate's skills corresponding to that skill word. In other words, the execution entity can select different candidates for recruitment assessment based on different skill words, improving the effectiveness of assessments for multiple skill words.

[0044] Continue to see Figure 3 , which shows a schematic diagram of an application scenario of the method for processing skill information according to the present disclosure. Figure 3 In this application scenario, the execution entity can obtain information 301 to be examined, including information about the position to be examined and the candidate to be examined. The execution entity inputs information 301 to be examined into a skill term generation model 302, which outputs skill terms 303. Skill terms 303 may include, but are not limited to, skill terms A, B, and C. The execution entity can then send skill terms 303 to a target candidate 304. Target candidate 304 can be one or multiple candidates, and this embodiment does not limit this.

[0045] The method for processing skill information provided by the above-mentioned embodiment of the present disclosure can determine the skill words corresponding to the information to be examined based on a pre-trained skill word generation model, and examine the skill information based on the skill words, thereby improving the accuracy of skill examination.

[0046] Continue to see Figure 4 , which shows a process 400 of an embodiment of the model training method according to the present disclosure. Figure 4 As shown, the model training method of this embodiment may include the following steps:

[0047] Step 401: Obtain information about samples to be inspected and labeled data of inspection results.

[0048] In this embodiment, the sample information to be examined can be the information on the position to be examined and the information on the object to be examined in the historical examination data, and the examination result annotation data can be the examination result information in the historical examination data that matches the information on the position to be examined and the information on the object to be examined, such as historical interview result information.

[0049] Step 402: Determine at least one sample skill word based on a preset skill word graph, sample information to be examined, and a model to be trained.

[0050] In this embodiment, the preset skill word graph includes multiple skill words and the association relationships between the skill words. The execution entity can determine at least one sample skill word to be output by the training model based on the preset skill word graph and the sample information to be examined.

[0051] The execution entity may input both the preset skill word graph and the sample information to be examined into the model to be trained, so that the model to be trained determines at least one sample skill word that matches the sample information to be examined from the preset skill word graph.

[0052] Step 403: Based on at least one sample skill word and the inspection result annotation data, the to-be-trained model is trained to obtain a trained skill word generation model.

[0053] In this embodiment, the execution entity can adjust the model parameters of the model to be trained based on the degree of match between the inspection result annotation data and the at least one sample skill word mentioned above, in response to determining that the degree of match is low, and repeat the iteration until the degree of match between at least one sample word output by the model to be trained and the inspection result annotation data is high, the preset convergence condition is met, and a trained skill word generation model is obtained.

[0054] The trained skill word generation model here can be applied to Figure 1 The described method for processing skill information can determine matching skill words based on the information to be examined.

[0055] The model training method provided by the above-mentioned embodiments of the present disclosure can also utilize a preset skill word graph, as well as sample information to be inspected and inspection result annotation data in historical inspection data, to train the model to be trained, obtain a trained skill word generation model, and realize the generation of skill words that match the information to be inspected.

[0056] Continue to see Figure 5 , which shows a process 500 of another embodiment of the model training method according to the present disclosure. Figure 5 As shown, the model training method of this embodiment may include the following steps:

[0057] Step 501: Obtain information about samples to be inspected and labeled data of inspection results.

[0058] In this embodiment, the sample information to be examined includes sample information on positions to be examined and sample information on candidates to be examined. The sample information on positions to be examined refers to information on positions from historical examinations, such as historical job requirements. The sample information on candidates to be examined refers to information on candidates from historical examinations, such as historical candidate resumes. The examination result annotation data may be interview result information from historical examinations, corresponding to the corresponding position information and candidate information from the historical examinations.

[0059] For the detailed description of step 501 , please refer to the detailed description of step 401 , which will not be repeated here.

[0060] Step 502: Determine a skill word graph based on the sample information to be examined and the examination result annotation data.

[0061] In this embodiment, the execution entity can extract each skill word from the sample information to be examined and the examination result annotation data, and establish the association relationship between each skill word based on the appearance position of each skill word in the sample information to be examined and the examination result annotation data to obtain a skill word graph.

[0062] In some optional implementations of this embodiment, a skill word graph is determined based on sample information to be examined, including: determining each candidate skill word from the sample information to be examined and the examination result annotation data; determining the connection information between each candidate skill word based on each candidate skill word, the sample information to be examined and the examination result annotation data; and determining the skill word graph based on the connection information between each candidate skill word and each candidate skill word.

[0063] In this implementation, the execution entity can perform text analysis on the sample inspection information and the inspection result annotated data to extract candidate skill terms. Optionally, the execution entity can use a combination of a recurrent neural network, an attention mechanism, and a replication mechanism to extract candidate skill terms from the sample inspection information and the inspection result annotated data. The replication mechanism is used to directly copy words outside the vocabulary, while the attention mechanism is used to focus more on key text and allocate attention resources. By combining a recurrent neural network, an attention mechanism, and a replication mechanism, the accuracy of identifying candidate skill terms can be improved.

[0064] The execution entity can then determine the connection information between each candidate skill term based on the candidate skill terms, the sample pending assessment information, and the assessment result annotation data. Optionally, the execution entity can determine the connection information between each candidate skill term based on the position of each candidate skill term within each piece of information and the associations between each piece of information. The information refers to the historical data of the pending assessment position information, the pending assessment subject information, and the assessment result annotation data within the sample pending assessment information and the assessment result annotation data.

[0065] In some other optional implementations of this embodiment, the connection information between each candidate skill word is determined based on each candidate skill word, the sample information to be examined, and the examination result annotation data, including: determining at least one group of sample data tuples based on the sample information to be examined and the examination result annotation data; for each group of sample data tuples, determining the connection information between the candidate skill words in the sample data tuple; and determining the connection information between each candidate skill word based on the connection information between the candidate skill words in each group of sample data tuples.

[0066] In this implementation, the sample pending examination information may include information about the position to be examined and information about the subject to be examined from multiple historical examination data, and the annotated examination result data may include historical interview result information from multiple historical examination data. The execution entity may determine at least one set of sample data tuples from the sample pending examination information and the annotated examination result data. For each set of sample data tuples, the sample data tuple includes corresponding information about the position to be examined, information about the subject to be examined, and historical interview result information.

[0067] For each set of sample data tuples, determining the connection information between candidate skill terms in the sample data tuple may include: for each piece of information in each set of sample data tuples, traversing the information using a preset sliding window, and establishing connection information between candidate skill terms within the information based on skill terms within the preset sliding window; and, for each piece of information in the same sample data tuple, establishing connection relationships between candidate skill terms across the text of the information. Based on the connection relationships between candidate skill terms within the information in each sample data tuple and the connection relationships between candidate skill terms across the text of the information, a preset skill term graph is generated. The information includes information about the position to be evaluated, information about the person to be evaluated, and information about historical interview results.

[0068] Step 503: Input the preset skill word graph and sample information to be examined into the model to be trained.

[0069] In this embodiment, the execution entity may input the above-mentioned skill word graph and sample information to be examined into the model to be trained, so that the model to be trained outputs at least one sample skill word.

[0070] Step 504: Determine label representation information based on the preset skill word graph.

[0071] In this embodiment, the execution entity can use a preset skill word graph to obtain label representation information, wherein the label representation information is used to represent each skill word.

[0072] Step 505: Determine topic representation information based on the preset skill word graph and sample information to be examined.

[0073] In this embodiment, the execution entity may map a preset skill word graph to a preset multidimensional space to obtain mapping data corresponding to the skill word graph. Furthermore, the execution entity may map the sample information to be examined to the preset multidimensional space to obtain mapping data corresponding to the sample information to be examined. Subsequently, the execution entity may integrate the mapping data corresponding to the skill word graph with the mapping data corresponding to the sample information to be examined to obtain topic representation information. The topic representation information is used to represent the text topics corresponding to the preset skill word graph and the sample information to be examined.

[0074] In some optional implementations of this embodiment, the following steps can also be performed: using a multi-layer perceptron, the mapping data corresponding to the skill word graph and the mapping data corresponding to the sample information to be examined are processed to obtain integrated data, and the integrated data is output for subsequent data analysis and processing based on the integrated data.

[0075] Step 506: Determine at least one sample skill word based on the tag representation information and the topic representation information.

[0076] In this embodiment, the execution guard may determine at least one sample skill word based on matching the tag representation information and the topic representation information. The sample skill word here may be a skill word in the tag representation information.

[0077] Step 507: Based on at least one sample skill word and the inspection result annotation data, the to-be-trained model is trained to obtain a trained skill word generation model.

[0078] In this embodiment, the skill word generation model can be a Bayesian latent variable model or a machine learning model in other existing technologies, which is not limited in this embodiment. For a detailed description of step 507, please refer to the detailed description of step 403, which will not be repeated here.

[0079] The model training method provided by the above-mentioned embodiments of the present disclosure can also extract skill terms from the sample information to be examined and the labeled data of the examination results, and determine the sample data tuples corresponding to the skill terms extracted from the sample information to be examined and the labeled data of the examination results. Based on the associations within and between the various information in each sample data tuple, the associations between the skill terms are determined to obtain a skill term graph. Using the skill term graph to assist model training can improve model accuracy. Furthermore, using topic representation information and label representation information to determine sample skill terms can improve the accuracy of sample skill term determination.

[0080] Further references Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for processing skill information. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to electronic devices such as terminal devices and servers.

[0081] like Figure 6 As shown, the apparatus 600 for processing skill information in this embodiment includes: an information acquisition unit 601 , a skill word determination unit 602 , and a skill word output unit 603 .

[0082] The information acquisition unit 601 is configured to acquire information to be examined.

[0083] The skill word determining unit 602 is configured to determine at least one skill word based on the information to be examined and a pre-trained skill word generation model.

[0084] The skill word output unit 603 is configured to output at least one skill word.

[0085] In some optional implementations of this embodiment, the information to be examined includes information about the position to be examined and information about the person to be examined.

[0086] In some optional implementations of this embodiment, the skill word output unit 603 is further configured to: determine the target recruitment object based on at least one skill word and recruitment object information; send at least one skill word to the target recruitment object, so that the target recruitment object conducts a skill assessment on the candidate based on the at least one skill word.

[0087] It should be understood that the units 601 to 603 described in the apparatus 600 for processing skill information are respectively the same as those in the reference Figure 2 Therefore, the operations and features described above for the method for processing skill information are also applicable to the device 600 and the units included therein, and will not be repeated here.

[0088] Further references Figure 7 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a model training device. Figure 4 Corresponding to the method embodiment shown, the device can be specifically applied to electronic devices such as terminal devices and servers.

[0089] like Figure 7 As shown, the model training device 700 of this embodiment includes: a sample acquisition unit 701, a sample skill word determination unit 702 and a model training unit 703.

[0090] The state acquisition unit 701 is configured to acquire information about samples to be inspected and labeled data of inspection results.

[0091] The sample skill word determining unit 702 is configured to determine at least one sample skill word based on a preset skill word graph, sample information to be examined, and a model to be trained.

[0092] The model training unit 703 is configured to train the to-be-trained model based on at least one sample skill word and the inspection result annotation data to obtain a trained skill word generation model.

[0093] In some optional implementations of this embodiment, it further includes: a skill word graph determination unit, configured to determine a skill word graph based on the sample to be examined information and the examination result annotation data.

[0094] In some optional implementations of this embodiment, the skill word graph determination unit is further configured to: determine each candidate skill word from the sample information to be examined and the examination result annotation data; determine the connection information between each candidate skill word based on each candidate skill word, the sample information to be examined and the examination result annotation data; determine the skill word graph based on the connection information between each candidate skill word and each candidate skill word.

[0095] In some optional implementations of this embodiment, the skill word graph determination unit is further configured to: determine at least one group of sample data tuples based on the sample information to be examined and the examination result annotation data; for each group of sample data tuples, determine the connection information between the candidate skill words in the sample data tuple; based on the connection information between the candidate skill words in each group of sample data tuples, determine the connection information between each candidate skill word.

[0096] In some optional implementations of this embodiment, the sample information to be examined includes sample information of positions to be examined and sample information of objects to be examined.

[0097] In some optional implementations of this embodiment, the sample skill word determination unit 702 is further configured to: input the preset skill word graph and sample information to be examined into the model to be trained; determine the label representation information based on the preset skill word graph; determine the topic representation information based on the preset skill word graph and the sample information to be examined; and determine at least one sample skill word based on the label representation information and the topic representation information.

[0098] It should be understood that the units 701 to 703 described in the model training device 700 are respectively the same as those in the reference Figure 4 Therefore, the operations and features described above for the model training method are also applicable to the device 700 and the units contained therein, and will not be repeated here.

[0099] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0100] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0101] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0102] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0103] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0104] The computing unit 801 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the method for processing skill information or the model training method. For example, in some embodiments, the method for processing skill information or the model training method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for processing skill information or the model training method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured in any other appropriate manner (eg, by means of firmware) to execute the method for processing skill information or the model training method.

[0105] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0111] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0112] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for processing skill information, comprising: Obtain information to be inspected; Based on the information to be examined and a pre-trained skill word generation model, at least one skill word is determined; wherein the skill word generation model is trained by the following steps: obtaining sample information to be examined and annotation data of examination results; inputting a preset skill word graph and the sample information to be examined into the model to be trained, the skill word graph being used to represent the association relationship between each skill word; determining label representation information based on the preset skill word graph; determining topic representation information based on the preset skill word graph and the sample information to be examined; determining at least one sample skill word based on the label representation information and the topic representation information; training the model to be trained based on the at least one sample skill word and the annotation data of the examination results to obtain the skill word generation model; The at least one skill word is output.

2. The method according to claim 1, wherein The information to be examined includes information about the position to be examined and information about the person to be examined.

3. The method according to claim 1, wherein The outputting the at least one skill word includes: Determining a target recruitment candidate based on the at least one skill word and the recruitment candidate information; The at least one skill word is sent to the target recruitment object, so that the target recruitment object performs a skill assessment on the candidate to be assessed based on the at least one skill word.

4. A model training method, comprising: Obtain information about samples to be inspected and labeled data on inspection results; Inputting a preset skill word graph and the sample information to be examined into the model to be trained, wherein the skill word graph is used to represent the association relationship between each skill word; Determining label representation information based on the preset skill word graph; Determining topic representation information based on the preset skill word graph and the sample to be examined information; Determining at least one sample skill word based on the tag representation information and the topic representation information; Based on the at least one sample skill word and the inspection result annotation data, the model to be trained is trained to obtain a trained skill word generation model.

5. The method according to claim 4, further comprising: The skill word graph is determined based on the sample to be examined information and the examination result annotation data.

6. The method according to claim 5, wherein: The determining the skill word graph based on the sample to be examined information includes: Determining candidate skill words from the sample to be examined information and the examination result annotated data; Determining connection information between the candidate skill words based on the candidate skill words, the sample to be examined information, and the examination result annotated data; The skill word graph is determined based on the respective candidate skill words and connection information between the respective candidate skill words.

7. The method according to claim 6, wherein: The determining, based on the candidate skill words, the sample to be examined information, and the examination result annotated data, of connection information between the candidate skill words includes: Determining at least one set of sample data tuples based on the sample to be inspected information and the inspection result labeled data; For each set of sample data tuples, determining connection information between candidate skill words in the sample data tuples; Based on the connection information between the candidate skill words in each group of sample data tuples, the connection information between the candidate skill words is determined.

8. The method according to claim 4, wherein The sample information to be examined includes sample information of positions to be examined and sample information of subjects to be examined.

9. A device for processing skill information, comprising: an information acquisition unit, configured to acquire information to be examined; A skill word determination unit is configured to determine at least one skill word based on the information to be examined and a pre-trained skill word generation model; wherein the skill word generation model is trained by the following steps: obtaining sample information to be examined and annotation data of examination results; inputting a preset skill word graph and the sample information to be examined into the model to be trained, the skill word graph being used to represent the association relationship between each skill word; determining label representation information based on the preset skill word graph; determining topic representation information based on the preset skill word graph and the sample information to be examined; determining at least one sample skill word based on the label representation information and the topic representation information; and training the model to be trained based on the at least one sample skill word and the annotation data of the examination results to obtain the skill word generation model. The skill word output unit is configured to output the at least one skill word.

10. The device according to claim 9, wherein The information to be examined includes information about the position to be examined and information about the person to be examined.

11. The device according to claim 9, wherein The skill word output unit is further configured to: Determining a target recruitment candidate based on the at least one skill word and the recruitment candidate information; The at least one skill word is sent to the target recruitment object, so that the target recruitment object performs a skill assessment on the candidate to be assessed based on the at least one skill word.

12. A model training device comprising: A sample acquisition unit is configured to acquire information about samples to be inspected and labeled data of inspection results; a sample skill word determination unit configured to input a preset skill word graph and the sample information to be examined into a model to be trained, wherein the skill word graph is used to represent the association relationship between each skill word; Determining label representation information based on the preset skill word graph; Determining topic representation information based on the preset skill word graph and the sample to be examined information; Determining at least one sample skill word based on the tag representation information and the topic representation information; The model training unit is configured to train the model to be trained based on the at least one sample skill word and the inspection result annotation data to obtain a trained skill word generation model.

13. The apparatus according to claim 12, further comprising: The skill word graph determining unit is configured to determine the skill word graph based on the sample to be examined information and the examination result annotation data.

14. The device according to claim 13, wherein The skill word graph determining unit is further configured to: Determining candidate skill words from the sample to be examined information and the examination result annotated data; Determining connection information between the candidate skill words based on the candidate skill words, the sample to be examined information, and the examination result annotated data; The skill word graph is determined based on the respective candidate skill words and connection information between the respective candidate skill words.

15. The device according to claim 14, wherein The skill word graph determining unit is further configured to: Determining at least one set of sample data tuples based on the sample to be inspected information and the inspection result labeled data; For each set of sample data tuples, determining connection information between candidate skill words in the sample data tuples; Based on the connection information between the candidate skill words in each group of sample data tuples, the connection information between the candidate skill words is determined.

16. The device according to claim 12, wherein The sample information to be examined includes sample information of positions to be examined and sample information of subjects to be examined.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

19. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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