CN117436432A - Service question-answer data processing method and device - Google Patents
Service question-answer data processing method and device Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The embodiment of the application provides a method and a device for processing service question-answer data, which relate to the field of natural language processing and can also be used in the financial field, and the method comprises the following steps: determining index weights of various indexes in the service question-answer data according to a preset judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; performing text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of a user corresponding to the service question-answering data; the method and the device can accurately determine the service proficiency of the staff and improve the accuracy of service question-answering.
Description
Technical Field
The application relates to the field of natural language processing and also can be used in the financial field, in particular to a business question-answering data processing method and device.
Background
In daily work, basic staff often throw business flow problems and business system problems encountered in daily work out of a platform, and a business expert in the field answers the problems to form a help mechanism.
In the prior art, more judgment of business expert personnel is to push corresponding questions to the expert hands in the related fields in a preset list according to a manual preset list and the label type selected for answering the questions.
The inventors found that the above approach has the following problems: the coverage of the preset list to the business expert is not comprehensive enough, because the preset list is generally set by each department, the preset list cannot be refined and implemented to other basic institutions, however, some colleagues with rich business experience in the basic institutions have the capability of the expert.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a method and a device for processing service question-answering data, which can accurately determine the service proficiency of staff and improve the accuracy of service question-answering.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a service question-answer data processing method, including:
determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
And carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
Further, before determining the index weight of each index in the service question-answer data according to the preset judgment matrix, the method includes:
and acquiring service question-answer data of a service question-answer platform, and performing index preprocessing and text preprocessing on the service question-answer data to obtain service question-answer data subjected to the index preprocessing and the text preprocessing.
Further, the performing index preprocessing and text preprocessing on the service question-answer data to obtain service question-answer data after the index preprocessing and the text preprocessing includes:
performing positive-negative conversion processing on each negative index in the service question-answer data according to a preset positive-negative index conversion rule to obtain service question-answer data subjected to positive-negative conversion processing;
and carrying out abnormality filtering processing and word segmentation and word stopping processing on the question and answer text in the service question and answer data according to a preset abnormality question and answer screening rule to obtain the service question and answer data after the abnormality filtering processing and the word segmentation and word stopping processing.
Further, the determining the index weight of each index in the service question-answer data according to the preset judgment matrix includes:
calculating expert scores of various indexes in the service question-answer data according to a preset judgment matrix;
and carrying out mathematical average calculation on the expert scores, and determining the index weight of each index according to the result of the mathematical average calculation.
Further, the text quality measurement is performed on the service question-answer data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and the determining of the service proficiency of the user corresponding to the service question-answer data includes:
determining index scores corresponding to the answer text attribute indexes, the answer text quality indexes, the answer text quantity indexes and the answer text field indexes in the service question and answer data according to a preset evaluation rule;
and determining the service proficiency of the user corresponding to the service question-answer data according to the index score.
Further, the determining, according to a preset evaluation rule, an index score corresponding to the answer text attribute index, the answer text quality index, the answer text quantity index and the answer text field index in the service question and answer data includes:
Determining index scores of answer text attribute indexes according to the working time length and post levels of the users corresponding to the service question-answer data;
determining answer reliability, answer relativity and answer intelligibility of the service question and answer data according to a natural semantic analysis algorithm, and determining index scores of answer text quality indexes according to the answer reliability, the answer relativity and the answer intelligibility;
determining index scores of the index of the number of the answer texts according to the number of the answer words and the number of the answer sentences of the service question-answer data;
and determining index scores of the indexes of the answering text fields according to the fields to which the answers of the service question and answer data belong.
Further, the text quality measurement is performed on the service question-answer data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and the service proficiency of the user corresponding to the service question-answer data is determined, which further comprises:
determining corresponding evaluation factors according to comprehensive evaluation of various indexes of the service question-answering data by a browsing user;
and performing factor synthesis on the evaluation factors according to a preset weighted average algorithm, and determining the service proficiency of the user corresponding to the service question-answer data.
In a second aspect, the present application provides a service question-answer data processing apparatus, including:
the index weight determining module is used for determining the index weight of each index in the service question-answer data according to a preset judging matrix;
the index synthesis processing module is used for carrying out index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
and the text quality measurement module is used for carrying out text quality measurement on the service question-answering data according to the various indexes and a preset nonlinear fuzzy comprehensive evaluation model and determining the service proficiency of the user corresponding to the service question-answering data.
Further, the method further comprises the following steps:
the preprocessing module is used for acquiring the service question-answer data of the service question-answer platform, and performing index preprocessing and text preprocessing on the service question-answer data to obtain the service question-answer data subjected to the index preprocessing and the text preprocessing.
Further, the preprocessing module includes:
the positive-negative conversion unit is used for carrying out positive-negative conversion processing on each negative index in the service question-answer data according to a preset positive-negative index conversion rule to obtain the service question-answer data subjected to the positive-negative conversion processing;
The word filtering and separating unit is used for carrying out abnormal filtering processing and word separation and word stopping processing on the question and answer text in the service question and answer data according to a preset abnormal question and answer screening rule to obtain service question and answer data after the abnormal filtering processing and the word separation and word stopping processing.
Further, the index weight determining module includes:
the expert scoring unit is used for calculating expert scores of various indexes in the service question-answer data according to a preset judgment matrix;
and the score calculation unit is used for carrying out mathematical average calculation on the expert scores and determining the index weight of each index according to the result of the mathematical average calculation.
Further, the text quality metric module includes:
the index score determining unit is used for determining index scores corresponding to the answer text attribute indexes, the answer text quality indexes, the answer text quantity indexes and the answer text field indexes in the service question and answer data according to a preset evaluation rule;
and the business proficiency determining unit is used for determining the business proficiency of the user corresponding to the business question-answer data according to the index score.
Further, the index score determining unit includes:
The attribute index determining subunit is used for determining index scores of the answer text attribute indexes according to the working time length and the post levels of the users corresponding to the service question and answer data;
the quality index determining subunit is used for determining the answer reliability, the answer relativity and the answer intelligibility of the service question and answer data according to a natural semantic analysis algorithm, and determining index scores of answer text quality indexes according to the answer reliability, the answer relativity and the answer intelligibility;
a quantity index determining subunit, configured to determine an index score of the answer text quantity index according to the answer word number and the answer sentence number of the service question and answer data;
and the domain index determining subunit is used for determining index scores of the domain indexes of the answer text according to the domain to which the answers of the service question and answer data belong.
Further, the text quality metric module further includes:
the user evaluation unit is used for determining corresponding evaluation factors according to comprehensive evaluation of various indexes of the service question-answer data by a browsing user;
and the weighting calculation unit is used for performing factor synthesis on the evaluation factors according to a preset weighted average algorithm and determining the service proficiency of the user corresponding to the service question-answer data.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the service question-answer data processing method when the program is executed.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the service questioning and answering data processing method.
In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the service questioning and answering data processing method.
According to the technical scheme, the application provides a method and a device for processing service question-answer data, and the index weights of various indexes in the service question-answer data are determined through a preset judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a service question-answer data processing method in an embodiment of the present application;
FIG. 2 is a second flow chart of a method for processing question-answer data according to an embodiment of the present application;
FIG. 3 is a third flow chart of a method for processing service question-answer data according to an embodiment of the present application;
FIG. 4 is a flowchart of a method for processing question-answer data according to an embodiment of the present application;
FIG. 5 is a flowchart of a method for processing question-answer data according to an embodiment of the present application;
FIG. 6 is a flowchart of a method for processing service questioning and answering data according to an embodiment of the present application;
FIG. 7 is one of the block diagrams of the service questioning and answering data processing apparatus in the embodiment of the present application;
FIG. 8 is a second block diagram of a service questioning and answering data processing device in an embodiment of the present application;
FIG. 9 is a third block diagram of a service questioning and answering data processing device in an embodiment of the present application;
FIG. 10 is a diagram showing the construction of a service questioning and answering data processing device in the embodiment of the present application;
FIG. 11 is a fifth block diagram of a service questioning and answering data processing device in an embodiment of the present application;
FIG. 12 is a diagram showing a structure of a service questioning and answering data processing device in the embodiment of the present application;
FIG. 13 is a diagram showing a construction of a service questioning and answering data processing device according to an embodiment of the present application;
fig. 14 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
In consideration of the problems existing in the prior art, the application provides a method and a device for processing service question-answer data, wherein the index weights of various indexes in the service question-answer data are determined through a preset judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
In order to accurately determine the service proficiency of an employee and improve the accuracy of service questions and answers, the application provides an embodiment of a service question and answers data processing method, referring to fig. 1, wherein the service question and answer data processing method specifically comprises the following contents:
step S101: determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
it is to be understood that the data sources of the present application may include at least three aspects: (1) the platform stores structure table data; (2) platform buried point data; and (3) analyzing and obtaining the platform text data.
Optionally, in the present application, each index in the service question-answer data may be as follows:
it can be understood that the determination method of each index weight includes a subjective weighting method and an objective weighting method. The subjective weighting is to weight the importance degree of the comprehensive evaluation result according to the index, and represents a method of Delphi method and analytic hierarchy process; the objective weighting is to determine the weight according to the information contained in the actual observed value of the index, and the representative method includes an entropy method, a coefficient of variation method and a Kamti bud method.
Optionally, a subjective weighting method may be adopted in the present application, where the main idea is to construct a judgment matrix according to the results of expert interviews, and then calculate the index weights under the scoring of each expert, and average to obtain the final weights.
Step S102: performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
optionally, the application can select a synthesis method of a second level index according to a correlation coefficient matrix and index weights of various indexes, and specifically, the indexes have smaller correlation and the weight difference is more important, so that the method is prone to additive synthesis; the correlation between indexes is large, and the multiplication synthesis is prone to be carried out when the correlation is sensitive to variation of the difference between indexes.
Step S103: and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
Optionally, the starting point of the attribute of the answer text is to evaluate the business proficiency of the respondent by utilizing the answer text, the quantity characteristics are used for measuring the information content of the answer text from three aspects of the number of answer words, the number of sentences and emotion scores, the information which is supposed to be expressed by the answer with more general words (word segmentation, useless word stop) and more sentences is considered to be more sufficient, the emotion scores are used for separating non-constructive answer content, the emotion of general sentences is stronger and biased to be negative, and the emotion scores of the sentences can be obtained by adopting an emotion tendency analysis model of an industry open source; the quality features evaluate the quality of the answer text in terms of reliability of the answer (measuring whether the answer content is correct), relevance (measuring whether the answer content is consistent with a question), intelligibility (measuring whether the answer content is easy to understand and translate), and the higher the quality score the more capable the respondent will be to output high quality answer content.
As can be seen from the above description, the method for processing service question-answer data provided in the embodiments of the present application can determine the index weights of the indexes in the service question-answer data by presetting a judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
In an embodiment of the service question-answer data processing method of the present application, the following may be further specifically included:
and acquiring service question-answer data of a service question-answer platform, and performing index preprocessing and text preprocessing on the service question-answer data to obtain service question-answer data subjected to the index preprocessing and the text preprocessing.
In an embodiment of the service question-answer data processing method of the present application, referring to fig. 2, the following may be further specifically included:
step S201: performing positive-negative conversion processing on each negative index in the service question-answer data according to a preset positive-negative index conversion rule to obtain service question-answer data subjected to positive-negative conversion processing;
specifically, index pretreatment: changing negative index into positive index, and eliminating dimension influence. Positive index calculation formula:negative index calculation formula: />
Step S202: and carrying out abnormality filtering processing and word segmentation and word stopping processing on the question and answer text in the service question and answer data according to a preset abnormality question and answer screening rule to obtain the service question and answer data after the abnormality filtering processing and the word segmentation and word stopping processing.
Optionally, text preprocessing: filtering answers which have no analysis value, such as picture answers, foreign language answers, answers with the length less than 5 words and the like; and performing word segmentation and word stopping removal processing on the text.
In an embodiment of the service question-answer data processing method of the present application, referring to fig. 3, the following may be further specifically included:
step S301: calculating expert scores of various indexes in the service question-answer data according to a preset judgment matrix;
step S302: and carrying out mathematical average calculation on the expert scores, and determining the index weight of each index according to the result of the mathematical average calculation.
It can be understood that the determination method of each index weight includes a subjective weighting method and an objective weighting method. The subjective weighting is to weight the importance degree of the comprehensive evaluation result according to the index, and represents a method of Delphi method and analytic hierarchy process; the objective weighting is to determine the weight according to the information contained in the actual observed value of the index, and the representative method includes an entropy method, a coefficient of variation method and a Kamti bud method.
Optionally, a subjective weighting method may be adopted in the present application, where the main idea is to construct a judgment matrix according to the results of expert interviews, and then calculate the index weights under the scoring of each expert, and average to obtain the final weights.
In an embodiment of the service question-answer data processing method of the present application, referring to fig. 4, the following may be further specifically included:
step S401: determining index scores corresponding to the answer text attribute indexes, the answer text quality indexes, the answer text quantity indexes and the answer text field indexes in the service question and answer data according to a preset evaluation rule;
Step S402: and determining the service proficiency of the user corresponding to the service question-answer data according to the index score.
Optionally, the starting point of the attribute of the answer text is to evaluate the business proficiency of the respondent by utilizing the answer text, the quantity characteristics are used for measuring the information content of the answer text from three aspects of the number of answer words, the number of sentences and emotion scores, the information which is supposed to be expressed by the answer with more general words (word segmentation, useless word stop) and more sentences is considered to be more sufficient, the emotion scores are used for separating non-constructive answer content, the emotion of general sentences is stronger and biased to be negative, and the emotion scores of the sentences can be obtained by adopting an emotion tendency analysis model of an industry open source; the quality features evaluate the quality of the answer text in terms of reliability of the answer (measuring whether the answer content is correct), relevance (measuring whether the answer content is consistent with a question), intelligibility (measuring whether the answer content is easy to understand and translate), and the higher the quality score the more capable the respondent will be to output high quality answer content.
In an embodiment of the service question-answer data processing method of the present application, referring to fig. 5, the following may be further specifically included:
step S501: determining index scores of answer text attribute indexes according to the working time length and post levels of the users corresponding to the service question-answer data;
Step S502: determining answer reliability, answer relativity and answer intelligibility of the service question and answer data according to a natural semantic analysis algorithm, and determining index scores of answer text quality indexes according to the answer reliability, the answer relativity and the answer intelligibility;
step S503: determining index scores of the index of the number of the answer texts according to the number of the answer words and the number of the answer sentences of the service question-answer data;
step S504: and determining index scores of the indexes of the answering text fields according to the fields to which the answers of the service question and answer data belong.
In an embodiment of the service question-answer data processing method of the present application, referring to fig. 6, the following may be further specifically included:
step S601: determining corresponding evaluation factors according to comprehensive evaluation of various indexes of the service question-answering data by a browsing user;
step S602: and performing factor synthesis on the evaluation factors according to a preset weighted average algorithm, and determining the service proficiency of the user corresponding to the service question-answer data.
Specifically, a comprehensive evaluation factor set is determined: u= { U 1 ,u 2 ,u 3 The expression = { reliability, correlation, intelligibility }, means that the information quality of an answer is measured in terms of reliability, correlation, intelligibility, respectively.
Establishing a factor weight set: the weight of the factors reflects the importance of the factors, and the weight of each factor is easily obtained through subjective weighting.
Selecting a set of evaluation grades: v= { V 1 ,v 2 ,v 3 The method comprises the steps of (1) dividing the evaluation grades according to the characteristics of text information quality, wherein the grade number is usually [3,7 ]]The number of the integers is more than the number of the odd numbers.
Single factor evaluation was performed and an evaluation matrix was established as follows:
[r 11 ,r 12 ,...,r 1m ]represents the membership to each class under the first evaluation factor, [ r ] 11 ,r 21 ,...,r p1 ]Representing the membership of all evaluation factors to the 1 st level; the membership degree represents the degree to which the text belongs to a certain grade under a certain evaluation factor, and the subjective fuzzy decision information is better reserved. By randomly burying the dots in the i-service product,after the information user (including questioner, praise and forwarder) is required to browse the answers, three evaluation indexes of the answers are evaluated, and the evaluation results are collected, so that the grade ratio approaches the membership degree.
Determining a synthesis factor: the fuzzy synthesis operator may employ a principal factor determination, principal factor highlighting, unbalanced averaging, weighted averaging operator. And for one answer text, synthesizing by using weighted average, balancing and considering all factors according to the weight of the factors, and obtaining the comprehensive membership of the evaluated text to each grade.
In order to accurately determine the proficiency of the employee and improve the accuracy of the service questions and answers, the application provides an embodiment of a service questions and answers data processing device for implementing all or part of the contents of the service questions and answers data processing method, referring to fig. 7, the service questions and answers data processing device specifically includes the following contents:
the index weight determining module 10 is configured to determine an index weight of each index in the service question-answer data according to a preset judgment matrix;
the index synthesis processing module 20 is configured to perform index synthesis processing according to the index weights and the correlation coefficient matrix of the indexes, so as to obtain indexes after the index synthesis processing;
and the text quality measurement module 30 is used for carrying out text quality measurement on the service question-answer data according to the various indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answer data.
As can be seen from the above description, the service question-answer data processing device provided in the embodiment of the present application can determine the index weights of the indexes in the service question-answer data by presetting a judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
In an embodiment of the service question-answering data processing device of the present application, referring to fig. 8, the following is further specifically included:
the preprocessing module 40 is configured to obtain service question and answer data of a service question and answer platform, and perform index preprocessing and text preprocessing on the service question and answer data to obtain service question and answer data after the index preprocessing and the text preprocessing.
In an embodiment of the service questioning and answering data processing apparatus of the present application, referring to fig. 9, the preprocessing module 40 includes:
a positive-negative conversion unit 41, configured to perform positive-negative conversion processing on each negative index in the service question-answer data according to a preset positive-negative index conversion rule, so as to obtain service question-answer data after the positive-negative conversion processing;
and the word filtering and separating unit 42 is used for performing exception filtering processing and word separation and word stopping processing on the question and answer text in the service question and answer data according to a preset exception question and answer screening rule to obtain service question and answer data after the exception filtering processing and the word separation and word stopping processing.
In an embodiment of the service questioning and answering data processing apparatus of the present application, referring to fig. 10, the index weight determining module 10 includes:
an expert scoring unit 11, configured to calculate expert scores of each index in the service question-answer data according to a preset judgment matrix;
And a score calculating unit 12, configured to perform mathematical average calculation on the expert score, and determine an index weight of each index according to a result of the mathematical average calculation.
In an embodiment of the service questioning and answering data processing apparatus of the present application, referring to fig. 11, the text quality measurement module 30 includes:
an index score determining unit 31, configured to determine, according to a preset evaluation rule, an index score corresponding to an answer text attribute index, an answer text quality index, an answer text quantity index, and an answer text field index in the service question and answer data;
a business proficiency determining unit 32, configured to determine a business proficiency of the user corresponding to the business question-answer data according to the index score.
In an embodiment of the service questioning and answering data processing apparatus of the present application, referring to fig. 12, the index score determining unit 31 includes:
an attribute index determining subunit 311, configured to determine an index score of the answer text attribute index according to the working duration and the post level of the user corresponding to the service question and answer data;
a quality index determining subunit 312, configured to determine answer reliability, answer correlation, and answer intelligibility of the service question and answer data according to a natural semantic analysis algorithm, and determine an index score of an answer text quality index according to the answer reliability, answer correlation, and answer intelligibility;
A number index determination subunit 313 for determining an index score of an answer text number index according to the number of answer words and the number of answer sentences of the service question and answer data;
a domain index determining subunit 314, configured to determine an index score of the answer text domain index according to the domain to which the answer of the service question and answer data belongs.
In an embodiment of the service questioning and answering data processing apparatus of the present application, referring to fig. 13, the text quality measurement module 30 further includes:
a user evaluation unit 33, configured to determine a corresponding evaluation factor according to comprehensive evaluation of various indexes of the service question-answer data by a browsing user;
and a weight calculation unit 34, configured to factor-synthesize the evaluation factors according to a preset weighted average algorithm, and determine the service proficiency of the user corresponding to the service question-answer data.
In order to accurately determine the service proficiency of staff and improve the accuracy of service questions and answers from the aspect of hardware, the application provides an embodiment of an electronic device for implementing all or part of contents in the service questions and answers data processing method, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the service question-answer data processing device and related equipment such as a core service system, a user terminal, a related database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the service question-answer data processing method and an embodiment of the service question-answer data processing device in the embodiments, and the contents thereof are incorporated herein, and are not repeated here.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical application, part of the service question-answer data processing method may be executed on the electronic device side as described above, or all operations may be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 14 is a schematic block diagram of a system configuration of an electronic device 9600 of an embodiment of the present application. As shown in fig. 14, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 14 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the service questioning and answering data processing method functions may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
step S101: determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
step S102: performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
step S103: and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
As can be seen from the above description, the electronic device provided in the embodiment of the present application determines the index weights of the indexes in the service question-answer data through the preset judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
In another embodiment, the service questioning and answering data processing apparatus may be configured separately from the central processor 9100, for example, the service questioning and answering data processing apparatus may be configured as a chip connected to the central processor 9100, and the service questioning and answering data processing method functions are implemented by control of the central processor.
As shown in fig. 14, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 14; in addition, the electronic device 9600 may further include components not shown in fig. 14, and reference may be made to the related art.
As shown in fig. 14, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiments of the present application further provide a computer readable storage medium capable of implementing all steps in the service questioning and answering data processing method in which the execution subject in the above embodiments is a server or a client, the computer readable storage medium storing thereon a computer program which when executed by a processor implements all steps in the service questioning and answering data processing method in which the execution subject in the above embodiments is a server or a client, for example, the processor implements the following steps when executing the computer program:
Step S101: determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
step S102: performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
step S103: and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
As can be seen from the above description, the computer readable storage medium provided in the embodiments of the present application determines the index weights of the indexes in the service question-answer data by presetting a judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
The embodiments of the present application further provide a computer program product capable of implementing all the steps in the service question-answer data processing method in which the execution subject in the above embodiments is a server or a client, where the computer program/instructions implement the steps of the service question-answer data processing method when executed by a processor, for example, the computer program/instructions implement the steps of:
step S101: determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
step S102: performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
step S103: and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
As can be seen from the above description, the computer program product provided in the embodiments of the present application determines the index weights of the indexes in the service question-answer data by presetting a judgment matrix; performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing; and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data, so that the service proficiency of staff can be accurately determined, and the service question-answering accuracy is improved.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of 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 a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. A method for processing service question-answer data, the method comprising:
determining index weights of various indexes in the service question-answer data according to a preset judgment matrix;
performing index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
and carrying out text quality measurement on the service question-answering data according to the indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data.
2. The method for processing service question-answer data according to claim 1, comprising, before determining the index weight of each index in the service question-answer data according to a preset judgment matrix:
and acquiring service question-answer data of a service question-answer platform, and performing index preprocessing and text preprocessing on the service question-answer data to obtain service question-answer data subjected to the index preprocessing and the text preprocessing.
3. The method for processing the service question-answer data according to claim 2, wherein the performing index preprocessing and text preprocessing on the service question-answer data to obtain service question-answer data after the index preprocessing and the text preprocessing includes:
Performing positive-negative conversion processing on each negative index in the service question-answer data according to a preset positive-negative index conversion rule to obtain service question-answer data subjected to positive-negative conversion processing;
and carrying out abnormality filtering processing and word segmentation and word stopping processing on the question and answer text in the service question and answer data according to a preset abnormality question and answer screening rule to obtain the service question and answer data after the abnormality filtering processing and the word segmentation and word stopping processing.
4. The method for processing the service question-answer data according to claim 1, wherein the determining the index weight of each index in the service question-answer data according to the preset judgment matrix includes:
calculating expert scores of various indexes in the service question-answer data according to a preset judgment matrix;
and carrying out mathematical average calculation on the expert scores, and determining the index weight of each index according to the result of the mathematical average calculation.
5. The method for processing the service question-answering data according to claim 1, wherein the text quality measurement is performed on the service question-answering data according to the various indexes and a preset nonlinear fuzzy comprehensive evaluation model, and determining the service proficiency of the user corresponding to the service question-answering data comprises:
Determining index scores corresponding to the answer text attribute indexes, the answer text quality indexes, the answer text quantity indexes and the answer text field indexes in the service question and answer data according to a preset evaluation rule;
and determining the service proficiency of the user corresponding to the service question-answer data according to the index score.
6. The method for processing the service question and answer data according to claim 5, wherein determining the index scores corresponding to the answer text attribute index, the answer text quality index, the answer text quantity index, and the answer text field index in the service question and answer data according to the preset evaluation rule comprises:
determining index scores of answer text attribute indexes according to the working time length and post levels of the users corresponding to the service question-answer data;
determining answer reliability, answer relativity and answer intelligibility of the service question and answer data according to a natural semantic analysis algorithm, and determining index scores of answer text quality indexes according to the answer reliability, the answer relativity and the answer intelligibility;
determining index scores of the index of the number of the answer texts according to the number of the answer words and the number of the answer sentences of the service question-answer data;
And determining index scores of the indexes of the answering text fields according to the fields to which the answers of the service question and answer data belong.
7. The method for processing the service question-answering data according to claim 1, wherein the text quality measurement is performed on the service question-answering data according to the various indexes and a preset nonlinear fuzzy comprehensive evaluation model, and the service proficiency of the user corresponding to the service question-answering data is determined, and the method further comprises:
determining corresponding evaluation factors according to comprehensive evaluation of various indexes of the service question-answering data by a browsing user;
and performing factor synthesis on the evaluation factors according to a preset weighted average algorithm, and determining the service proficiency of the user corresponding to the service question-answer data.
8. A service question-answering data processing apparatus, comprising:
the index weight determining module is used for determining the index weight of each index in the service question-answer data according to a preset judging matrix;
the index synthesis processing module is used for carrying out index synthesis processing according to the index weights and the correlation coefficient matrixes of the indexes to obtain the indexes after the index synthesis processing;
and the text quality measurement module is used for carrying out text quality measurement on the service question-answering data according to the various indexes and a preset nonlinear fuzzy comprehensive evaluation model and determining the service proficiency of the user corresponding to the service question-answering data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for processing question-and-answer data according to any one of claims 1 to 7 when said program is executed by said processor.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the service question-answer data processing method of any one of claims 1 to 7.
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