CN112418779A - Online self-service interviewing method based on natural language understanding - Google Patents
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Abstract
The invention discloses an online self-help interview method based on natural language understanding, which comprises the following steps: setting interview questions, question answers and question weights to generate an interview question library; collecting resume information, wherein the resume information comprises resume photos; setting an evaluation rule based on the resume information, performing resume evaluation to obtain a resume evaluation result, and screening out qualified resumes according to the resume evaluation result; the resume qualifiers can continuously participate in network video interviews, output questions and start to ask questions, store the answer content of the interviewers in a system, perform semantic matching on the answer content and the answers to the questions, obtain related questions in the interview question bank based on the semantic matching, and start to ask the next question; and obtaining the answer accuracy of the interviewer based on the semantic matching, and making interview evaluation.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to an online self-service interview method based on natural language understanding.
Background
With the rapid development of computer technology, all industries have developed the wave of promoting the development of the industries by applying the computer technology.
In recent years, the number of people in business is greatly increased, and talents play a vital role in enterprise development for large-scale enterprises. During the peak period of enterprise recruitment, a large number of resumes are often received. At present, the general flow of the recruiters of most enterprises is that manual work is used for initially screening resumes, corresponding screened personnel are intensively organized to conduct initial interviews, and further personnel are selected according to evaluation after the initial interviews to enter the next interview.
In the process of conducting preliminary interviewing, a large amount of manpower and time resources are consumed, questions asked by interviewees are inconsistent, and no uniform evaluation standard exists. In addition, the interviewer and the large number of applicants can have difficulty determining the point in time to focus the interview.
Disclosure of Invention
The embodiment of the application provides an online self-help interview method based on natural language understanding, which is used for solving the following technical problems in the prior art:
the preliminary interviewing workload is large, the interviewing efficiency is low, unnecessary resource waste is caused, the interviewing evaluation has no unified evaluation standard, both parties are difficult to determine the appropriate interviewing time, and the talents are easy to lose and miss the appropriate working opportunities.
An embodiment of the present specification provides an online self-help interviewing method based on natural language understanding, including:
setting interview questions, question answers and question weights to generate an interview question library;
collecting resume information, wherein the resume information comprises resume photos;
setting an evaluation standard based on the resume information, performing resume evaluation to obtain a resume evaluation result, and screening out qualified resumes according to the resume evaluation result;
the resume qualifiers can continuously participate in network video interviews, output questions and start to ask questions, store the answer content of the interviewers in a system, perform semantic matching on the answer content and the answers to the questions, obtain related questions in the interview question bank based on the semantic matching, and start to ask the next question;
and obtaining the answer accuracy of the interviewer based on the semantic matching, and making interview evaluation.
Optionally, the setting of the evaluation criterion specifically includes:
setting five evaluation indexes of graduation colleges and universities grade, professional correlation degree, working age, working experience and project experience;
and setting evaluation index weights for the five evaluation indexes.
Optionally, after screening out the biographical qualifiers according to the biographical evaluation result, before outputting a question and starting to ask a question, the method further includes:
acquiring the resume photo;
identifying and authenticating the interviewer and the resume photo by using a face identification technology;
if the authentication is successful, the interviewer can continue to participate in the network video interview.
Optionally, the performing resume evaluation specifically includes:
and performing resume evaluation according to a threshold weight evaluation method and a generalized regression neural network model evaluation method.
Optionally, the threshold weight evaluation method specifically includes:
acquiring the resume information corresponding to the evaluation index, and comparing the resume information with the requirement of the applicable post to obtain a comparison result corresponding to the evaluation index;
and performing weighted calculation on the comparison result based on the evaluation index weight to obtain a resume evaluation score.
Optionally, the generalized regression neural network model evaluation method specifically includes:
training a generalized regression neural network model;
and calling a generalized regression neural network model to predict the matching degree of the current resume and the applied post, and finishing the resume evaluation.
Optionally, before outputting the topic start question, the method further comprises:
and acquiring a certain number of questions from the interview question library according to interview posts, and performing voice conversion on the questions.
Optionally, before storing the contents of the interviewer responses to the system, the method further comprises:
and performing character conversion on the answer content of the interviewer to obtain the answer content in a text format.
Optionally, the semantically matching the answer content with the question answer specifically includes:
and applying the attention mechanism to a parallel convolutional neural network by adopting a convolutional neural network semantic matching algorithm of the attention mechanism, and performing semantic matching on the answer content and the question answer.
Optionally, the setting of the interview question specifically includes:
and setting corresponding questions according to different posts, wherein the questions are divided into a question to be answered and a question to be answered.
The invention can improve the interview efficiency, make the interview more flexible, save labor and time and avoid unnecessary resource waste.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application in a non-limiting sense. In the drawings:
fig. 1 is a schematic flow chart of an online self-help interview method based on natural language understanding according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a resume evaluation method provided in an embodiment of the present application;
fig. 3 is a flow chart of a method for providing answer evaluation according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the following description of the present disclosure will be made in detail and completely with reference to the embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in the description belong to the protection scope of the present application.
The embodiment of the application provides an online self-help interview method based on natural language understanding, which is specifically explained as follows:
fig. 1 is a schematic flowchart of an online self-help interview method based on natural language understanding according to an embodiment of the present application, where the flowchart in fig. 1 includes the following steps:
s101: setting interview questions, question answers and question weights to generate an interview question library;
s102: collecting resume information, wherein the resume information comprises resume photos;
s103: setting an evaluation standard based on the resume information, performing resume evaluation to obtain a resume evaluation result, and screening out qualified resumes according to the resume evaluation result;
s104: the resume qualifiers can continuously participate in network video interviews, output questions and start to ask questions, store the answer content of the interviewers in a system, perform semantic matching on the answer content and the answers to the questions, obtain related questions in the interview question bank based on the semantic matching, and start to ask the next question;
s105: and obtaining the answer accuracy of the interviewer based on the semantic matching, and making interview evaluation.
An online self-help interview method based on natural language understanding is realized by an online intelligent interview system, and the system comprises question bank setting, resume collection, resume evaluation, video interview and interview evaluation.
Item bank setting
Setting interview questions, question answers and question weights to generate an interview question library. The question bank setting means that a plurality of interview problems are added through the system according to a certain recruitment requirement by enterprise manpower.
Considering that the application requirements of different posts are different, corresponding questions can be set according to the posts, meanwhile, considering that some questions may not be necessarily answered for the posts applied, and also may relate to the problem that the personal privacy of the interviewer is not easy to answer, but the enterprise human is considered to know better, the questions can be set as answer-selecting questions, so that the interview system is more humanized, and the user experience is improved. Therefore, the interview problem setting method specifically comprises the following steps: and setting corresponding questions according to different posts, wherein the questions are divided into questions to be answered and questions to be answered.
Second, resume collection
Resume information is collected, the resume information including resume photographs. Resume collection refers to the fact that an applicant fills in a resume through a system, selects an application post, and inputs personal basic information including name, gender, age, photos, graduation colleges, specialty, working age, working experience, project experience and the like. The resume photo can upload a photo in a picture format to the system, and can also collect a photo of an interviewer through the camera and input the photo into the system.
Third, resume evaluation
And the resume evaluation refers to the system performing preliminary evaluation on the resume according to the entered resume information. Setting an evaluation standard based on the resume information, performing resume evaluation to obtain a resume evaluation result, and screening out qualified resumes according to the resume evaluation result.
Setting an evaluation standard, specifically comprising: setting five evaluation indexes of graduation colleges and universities grade, professional correlation degree, working age, working experience and project experience; and setting evaluation index weights for the five evaluation indexes. The setting of the evaluation indexes has flexibility, and enterprises can flexibly set the evaluation indexes according to the requirements of the recruitment posts. Meanwhile, in consideration of the fact that the importance degree of each index to different posts is different in an actual situation, the inventor takes a measure of setting the evaluation index weight for the evaluation index so as to conveniently and accurately screen out talents which meet the post requirements better.
And performing resume evaluation, specifically comprising: and performing resume evaluation according to a threshold weight evaluation method and a generalized regression neural network model evaluation method.
Firstly, the threshold weight evaluation method specifically comprises the following steps: acquiring the resume information corresponding to the evaluation index, and comparing the resume information with the requirement of the applicable post to obtain a comparison result corresponding to the evaluation index; and performing weighted calculation on the comparison result based on the evaluation index weight to obtain a resume evaluation score.
And the threshold weight evaluation method is used for analyzing resume information based on natural language understanding service. Setting weights for all evaluation indexes in advance, identifying the grade of the college and university based on natural language understanding service, such as a data dictionary of the college and university, extracting keywords according to the project experience description, and comparing the similarity with the requirement of the applicable post. And performing weighted calculation on each item of data according to the matching result, and outputting the resume evaluation score.
Secondly, the generalized regression neural network model evaluation method specifically comprises the following steps: training a generalized regression neural network model; and calling a generalized regression neural network model to predict the matching degree of the current resume and the applied post, and finishing the resume evaluation.
The generalized regression neural network model evaluation method comprises generalized regression neural network model training and generalized regression neural network model application. The generalized regression neural network model training comprises the following steps:
(1) resume samples are determined. In a large amount of resume information quantization data, attributes of resume information are subjected to quantization evaluation and converted into a group of vectors. And setting proportions to respectively extract the qualified resume data and the unqualified resume data to form a resume data test group.
(2) And (4) preprocessing data. And preprocessing the quantized data and normalizing the data.
(3) And training a generalized regression neural network model. And (3) creating an optimal smoothing parameter of the generalized regression neural network, for example, the optimal smoothing parameter is spread [ 0.050.20.40.60.8 ], continuously adjusting a smoothing factor according to an error between the resume information data output in a prediction mode and the qualified resume data output in an actual mode until the resume information data output in the prediction mode and the qualified resume data output in the actual mode reach the minimum error after multiple iterations, finishing model training, and outputting a related prediction result.
And after the training of the generalized regression neural network model is completed, calling the model to predict the matching degree of each resume and the job requirement of the current applicable post, and completing the resume evaluation.
Fig. 2 is a flowchart of a resume evaluation method provided in an embodiment of the present application, where resume data is first imported, data splitting is performed, qualified resumes and unqualified resumes are screened, data normalization processing is performed, a generalized regression neural network model is created, generalized regression neural network model training is performed, an optimal smoothing parameter is determined, and finally, the model is applied to complete resume evaluation.
Four, video interview
The resume qualifiers can continue to participate in the network video interview, output questions to start questioning, store the answer content of the interviewer to the system, perform semantic matching on the answer content and the answers to the questions, obtain the relevant questions in the interview question bank based on the semantic matching, and start to ask the next question.
After screening resume qualifiers according to the resume evaluation result, before outputting questions and starting to ask questions, the method further comprises the following steps: acquiring the resume photo; identifying and authenticating the interviewer and the resume photo by using a face identification technology; if the authentication is successful, the interviewer can continue to participate in the network video interview. The interviewer can be continuously participated after the same person is determined through the face recognition, if the same person is not found through the face recognition, the system sends out violation warning to punish the interviewer, and for example, the interviewer can be pulled into a blacklist. Therefore, the fairness of interviews can be guaranteed, cheating is avoided, and a clean online interview environment is created.
In order to increase the interactivity between the system and the interviewer in the self-help interviewing process, before questions are output and questions are asked, a certain number of questions are obtained from the interview question bank according to interview posts, and voice conversion is carried out on the questions. After listening to the questions, the interviewee can answer the questions once asking, so that the interviewee seems like an interview on the spot, and the user experience is improved.
Before storing the content of the interviewer responses to the system, the method further comprises: and performing character conversion on the answer content of the interviewer to obtain the answer content in a text format. The reason for the conversion into text is to facilitate the system to capture keywords and perform semantic matching with the question answers. Performing semantic matching on the answer content and the question answers, and specifically comprising: and applying the attention mechanism to a parallel convolutional neural network by adopting a convolutional neural network semantic matching algorithm of the attention mechanism, and performing semantic matching on answer contents and question answers.
Fifth, evaluation of the interview
And obtaining the answer accuracy of the interviewer based on the semantic matching, and making answer evaluation on the interviewer.
Fig. 3 is a flowchart of an answer evaluation method provided in the embodiment of the present application, in which face recognition and verification are performed first, question extraction is started after no error is determined, semantic matching is performed according to the content of an answer, so that an answer is asked, and finally an answer evaluation is given.
And integrating the resume evaluation and the answer evaluation, and finally giving an interview evaluation score to finish the interview evaluation.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalents, improvements, etc. that come within the spirit of the disclosure are intended to be included within the scope of the claims of this disclosure.
Claims (10)
1. An online self-help interview method based on natural language understanding is characterized by comprising the following steps:
setting interview questions, question answers and question weights to generate an interview question library;
collecting resume information, wherein the resume information comprises resume photos;
setting an evaluation rule based on the resume information, performing resume evaluation to obtain a resume evaluation result, and screening out qualified resumes according to the resume evaluation result;
the resume qualifiers can continuously participate in network video interviews, output questions and start to ask questions, store the answer content of the interviewers in a system, perform semantic matching on the answer content and the answers to the questions, obtain related questions in the interview question bank based on the semantic matching, and start to ask the next question;
and obtaining the answer accuracy of the interviewer based on the semantic matching, and making interview evaluation.
2. The method according to claim 1, wherein the setting of the evaluation rule specifically includes:
setting five evaluation indexes of graduation colleges and universities grade, professional correlation degree, working age, working experience and project experience;
and setting evaluation index weights for the five evaluation indexes.
3. The method according to claim 1, wherein after screening resume qualifiers according to the resume evaluation result, before outputting a question to start questioning, the method further comprises:
acquiring the resume photo;
identifying and authenticating the interviewer and the resume photo by using a face identification technology;
if the authentication is successful, the interviewer can continue to participate in the network video interview.
4. The method according to claim 2, wherein the performing resume evaluation specifically comprises:
and performing resume evaluation according to a threshold weight evaluation method and a generalized regression neural network model evaluation method.
5. The method according to claim 4, wherein the threshold weight evaluation method specifically comprises:
acquiring the resume information corresponding to the evaluation index, and comparing the resume information with the requirement of the applicable post to obtain a comparison result corresponding to the evaluation index;
and performing weighted calculation on the comparison result based on the evaluation index weight to obtain a resume evaluation score.
6. The method according to claim 4, wherein the generalized regression neural network model evaluation method specifically comprises:
training a generalized regression neural network model;
and calling a generalized regression neural network model to predict the matching degree of the current resume and the applied post, and finishing the resume evaluation.
7. The method of claim 1, wherein before outputting a topic start question, the method further comprises:
and acquiring a certain number of questions from the interview question library according to interview posts, and performing voice conversion on the questions.
8. The method of claim 1, wherein prior to storing the content of the interviewer responses to the system, the method further comprises:
and performing character conversion on the answer content of the interviewer to obtain the answer content in a text format.
9. The method according to claim 8, wherein the semantically matching the answer content with the question answer comprises:
and applying the attention mechanism to a parallel convolutional neural network by adopting a convolutional neural network semantic matching algorithm of the attention mechanism, and performing semantic matching on the answer content and the question answer.
10. The method according to claim 1, wherein the setting of interview questions specifically comprises:
and setting corresponding questions according to different posts, wherein the questions are divided into a question to be answered and a question to be answered.
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| CN117236911B (en) * | 2023-11-13 | 2024-02-02 | 贵州优特云科技有限公司 | Interview evaluation method and system based on artificial intelligence |
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Application publication date: 20210226 |