CN111061870A - Article quality evaluation method and device - Google Patents
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Abstract
The embodiment of the disclosure discloses a method and a device for evaluating the quality of an article, relates to the technical field of computers, and can solve the technical problems that most news manuscripts are time-efficient and are difficult to review by a small amount of manual work in the prior art. The method of the embodiment of the disclosure mainly comprises: calculating semantic feature vectors of the articles to be evaluated according to the first neural network model; and classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated. Compared with the prior art, the quality evaluation process of the article to be evaluated does not need manual examination, the automation of the article quality evaluation is realized, the evaluation efficiency of the article quality can be greatly improved, and the quality evaluation work of massive manuscripts to be evaluated can be completed in time.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for evaluating the quality of an article.
Background
Along with the popularization of the internet of things, more common people join in the line of network published articles.
Taking the news industry as an example, as the number of professional reporters is limited, and the demand of network news is greatly increased, more free contributors are added into the rank of news contribution. Most of the free contributors are not trained in the writing of news manuscripts, which causes the problem of uneven quality among manuscripts written by the free contributors. These problems greatly increase the workload of news reviewers, and lead to most news manuscripts and future and serious auditing to be sent to the network for news publishing, so that a great amount of news manuscripts with low quality exist in the network, and poor reading experience is brought to users.
Disclosure of Invention
The method and the device solve the technical problems that in the prior art, manuscripts to be audited are large in number, most news manuscripts are timeliness, and auditing cannot be completed timely through a small amount of manpower.
The embodiment of the disclosure mainly provides the following technical scheme:
in a first aspect, an embodiment of the present disclosure provides an article quality evaluation method, including:
calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated.
In some embodiments, prior to classifying the semantic feature vectors according to a second neural network model, the method further comprises:
calculating semantic feature vectors of the article to be trained according to the first neural network model;
and performing model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
In some embodiments, calculating semantic feature vectors of the article to be evaluated according to the first neural network model comprises:
obtaining semantic feature vectors corresponding to characters of the article to be evaluated according to the first neural network model;
and calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
In some embodiments, calculating semantic feature vectors of the article to be evaluated according to the first neural network model comprises:
and obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
In some embodiments, classifying the semantic feature vectors according to a second neural network model comprises:
classifying the articles to be evaluated according to a third neural network model;
and selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors, and evaluating to obtain the quality of the article to be evaluated.
In a second aspect, an embodiment of the present disclosure provides an apparatus for evaluating quality of an article, including:
the calculation unit is used for calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and the evaluation unit is used for classifying the semantic feature vectors according to the second neural network model and evaluating the quality of the article to be evaluated.
In some embodiments, the computing unit is further configured to compute semantic feature vectors of the article to be trained according to the first neural network model;
and the model training unit is used for carrying out model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
In some embodiments, the computing unit comprises:
the acquisition module is used for acquiring semantic feature vectors corresponding to the characters of the article to be evaluated according to the first neural network model;
and the first calculation module is used for calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
In some embodiments, the computing unit comprises:
and the second calculation module is used for obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
In some embodiments, the evaluation unit comprises:
the classification module is used for classifying the article to be evaluated according to a third neural network model;
and the evaluation module is used for selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors and evaluating the quality of the article to be evaluated.
In a third aspect, an embodiment of the present disclosure provides a storage medium, where the storage medium includes a stored program, and when the program runs, a device on which the storage medium is located is controlled to execute the method for evaluating the quality of an article according to the first aspect.
The storage medium may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
In a fourth aspect, embodiments of the present disclosure provide an apparatus for evaluating quality of an article of text, the apparatus comprising a storage medium; and one or more processors, the storage medium coupled with the processors, the processors configured to execute program instructions stored in the storage medium; the program instructions when executed perform the method for evaluating the quality of an article according to the first aspect.
By the technical scheme, the method and the device for evaluating the quality of the article provided by the technical scheme at least have the following advantages:
in the technical scheme provided by the embodiment of the disclosure, after writing an article to be evaluated, a drafter can calculate the semantic feature vector of the article through the first neural network model, then can input the calculated semantic feature vector into the second neural network model to obtain a classification result, and evaluate the quality of the article to be evaluated according to the classification result. Compared with the prior art, the quality evaluation process of the article to be evaluated does not need manual examination, the automation of the article quality evaluation is realized, the evaluation efficiency of the article quality can be greatly improved, and the quality evaluation work of massive manuscripts to be evaluated can be completed in time.
The foregoing description is only an overview of the embodiments of the present disclosure, and in order to make the technical means of the embodiments of the present disclosure more clearly understood, the embodiments of the present disclosure may be implemented in accordance with the content of the description, and in order to make the foregoing and other objects, features, and advantages of the embodiments of the present disclosure more clearly understood, the following detailed description of the embodiments of the present disclosure is given.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the present disclosure. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow chart of a method for evaluating the quality of an article provided by an embodiment of the present disclosure;
FIG. 2 shows a flow chart of another method for evaluating the quality of an article provided by an embodiment of the present disclosure;
FIG. 3 is a block diagram illustrating the components of an article quality assessment apparatus provided by an embodiment of the present disclosure;
fig. 4 shows a block diagram of a quality evaluation apparatus for a specific article according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In a first aspect, an embodiment of the present disclosure provides a method for evaluating the quality of an article, as shown in fig. 1, the method includes:
110. calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
the first neural network model is used for calculating semantic feature vectors of articles, and the semantic feature vectors of the articles to be evaluated can be calculated by inputting the articles to be evaluated into the first neural network model.
In some implementations, the calculating the semantic feature vectors of the article to be evaluated according to the first neural network model is a model for obtaining the semantic feature vectors of each character in the article to be evaluated, and includes: obtaining semantic feature vectors corresponding to characters of the article to be evaluated according to the first neural network model; and calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated. Calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated can adopt an averaging calculation mode, and the method comprises the following steps: and accumulating and averaging the semantic feature vectors of each character to obtain the semantic feature vectors of the articles to be evaluated.
In some implementations, calculating the semantic feature vector of the article to be evaluated according to the first neural network model may adopt a weighted model calculation mode, including: and obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word. Specifically, in the weighting calculation, a Transformer model structure may be adopted to obtain the semantic feature vector of the first text according to the semantic feature vector of each word. The attribute-based Transformer model abandons the inherent fixed form and does not use any CNN or RNN structure, can work highly in parallel and has higher semantic analysis performance.
Generally, the articles to be evaluated can be classified into different categories, such as news manuscripts, network novels, real-time comments and the like, and the different categories correspond to different first neural network models. Of course, the articles to be evaluated may not be classified, and the embodiments of the present application are not limited.
The semantic feature vectors of the articles to be evaluated can be calculated in real time by adopting the first neural network model, and compared with an artificial quality evaluation method, the quality evaluation of the articles with higher time limit can be realized.
120. And classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated.
The second neural network model is a classification model used for calculating the quality corresponding to the semantic feature vector of the article to be evaluated, and the quality corresponding to the semantic feature vector can be calculated by the second neural network model by inputting the semantic feature vector into the second neural network model, namely the quality of the article to be evaluated is evaluated. Generally, the quality of the article to be evaluated can include pass and fail, and it is easily understood that the quality of the article to be evaluated can also be set to any other form according to different requirements, for example, the quality of the article to be evaluated can include good, good and bad.
In the technical scheme provided by the embodiment of the disclosure, after writing an article to be evaluated, a drafter can calculate the semantic feature vector of the article through the first neural network model, then can input the calculated semantic feature vector into the second neural network model to obtain a classification result, and evaluate the quality of the article to be evaluated according to the classification result. Compared with the prior art, the quality evaluation process of the article to be evaluated does not need manual examination, the automation of the article quality evaluation is realized, the evaluation efficiency of the article quality can be greatly improved, and the quality evaluation work of massive manuscripts to be evaluated can be completed in time.
In a second aspect, an embodiment of the present disclosure provides an article quality evaluation method, in which a step of training a second neural network model in the article quality evaluation method of the first aspect is disclosed, as shown in fig. 2, the method includes:
210. calculating semantic feature vectors of the article to be trained according to the first neural network model;
the first neural network model is used for calculating semantic feature vectors of articles, and the semantic feature vectors of the articles to be trained can be obtained through calculation by inputting the articles to be trained into the first neural network model.
In some implementations, the first neural network model is a model for obtaining semantic feature vectors of characters in an article, and the calculating of the semantic feature vectors of the article to be trained according to the first neural network model includes: obtaining semantic feature vectors corresponding to characters of the article to be trained according to the first neural network model; and calculating the semantic feature vector of the article to be trained according to the semantic feature vector corresponding to each character in the article to be trained. Calculating the semantic feature vector of the article to be trained according to the semantic feature vector corresponding to each character in the article to be trained, wherein an averaging calculation mode can be adopted, and the method comprises the following steps: and accumulating and averaging the semantic feature vectors of each character to obtain the semantic feature vectors of the article to be trained.
In some implementations, calculating the semantic feature vector of the article to be trained according to the first neural network model may employ a weighted model calculation method, including: and obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be trained through the weight and the semantic feature vector of each word. Specifically, in the weighting calculation, a Transformer model structure can be adopted to obtain the semantic feature vector of the first text according to the semantic feature vector of each word, the Transformer model based on the Attention abandons an inherent fixed form and does not use any CNN or RNN structure, the model can work in a highly parallel mode, and the semantic analysis performance is high.
Generally, the articles to be trained can be classified into different categories, such as news manuscripts, network novels, real-time comments, and the like, and the different categories correspond to different first neural network models. Of course, the articles to be trained may not be classified, and the embodiment of the present application is not limited. The semantic feature vector of the article to be trained can be calculated in real time by adopting the first neural network model.
220. And carrying out model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
A large number of articles to be trained can be collected for each quality of article, the semantic feature vector (X) corresponding to each article is calculated through step 210, each article to be trained is labeled, and the quality (Y) corresponding to the article to be trained is labeled by the label, so that a training data set (X, Y) is formed. A neural network classification model is trained on the training dataset (X, Y). The trained neural network classification model is a second neural network model and can be used for evaluating the quality of the article to be evaluated.
In implementation, for articles to be trained of different classifications, model training can be performed according to semantic feature vectors of the articles to be trained of different classifications and quality labels of the articles to be trained of different classifications, so as to obtain second neural network models of different classifications.
230. Calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
240. and classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated.
In implementation, for articles to be trained with different classifications, a second neural network model corresponding to the classification with different classifications is provided, and the articles to be evaluated can be classified according to a third neural network model; and selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors, and evaluating to obtain the quality of the article to be evaluated. Under the condition that the types of the articles to be evaluated are wide, the quality evaluation of the articles to be evaluated can be carried out by adopting second neural network models of different types.
In a third aspect, an embodiment of the present disclosure provides an article quality evaluation apparatus, as shown in fig. 3, the apparatus including:
the calculation unit 10 is used for calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and the evaluation unit 20 is configured to classify the semantic feature vectors according to a second neural network model, and evaluate to obtain the quality of the article to be evaluated.
In some embodiments, as shown in fig. 4, the computing unit 10 is further configured to compute semantic feature vectors of the article to be trained according to the first neural network model;
and the model training unit 30 is configured to perform model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained, so as to obtain the second neural network model.
In some embodiments, the computing unit 10 comprises:
an obtaining module 11, configured to obtain, according to the first neural network model, a semantic feature vector corresponding to a text of the article to be evaluated;
the first calculating module 12 is configured to calculate a semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each text in the article to be evaluated.
In some embodiments, the computing unit 10 comprises:
and the second calculating module 13 is configured to obtain a weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculate the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
In some embodiments, the evaluation unit 20 comprises:
the classification module 21 is configured to classify the article to be evaluated according to a third neural network model;
and the evaluation module 22 is configured to select a second neural network model corresponding to the category to which the article to be evaluated belongs, classify the semantic feature vectors, and evaluate to obtain the quality of the article to be evaluated.
In a fourth aspect, an embodiment of the present disclosure provides a storage medium, where the storage medium includes a stored program, and when the program runs, a device where the storage medium is located is controlled to execute the method for evaluating the quality of the article according to the first aspect or the second aspect.
The storage medium may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
In a fifth aspect, embodiments of the present disclosure provide an apparatus for evaluating quality of an article, the apparatus including a storage medium; and one or more processors, the storage medium coupled with the processors, the processors configured to execute program instructions stored in the storage medium; the program instructions when executed perform the method of assessing the quality of an article of the first or second aspect.
In a sixth aspect, a1 is a method for evaluating the quality of an article, including:
calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated.
A2, according to the method for quality assessment of an article described in A1, before classifying the semantic feature vectors according to a second neural network model, the method further comprising:
calculating semantic feature vectors of the article to be trained according to the first neural network model;
and performing model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
A3, according to the article quality evaluation method of A1, calculating semantic feature vectors of the article to be evaluated according to the first neural network model, including:
obtaining semantic feature vectors corresponding to characters of the article to be evaluated according to the first neural network model;
and calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
A4, according to the article quality evaluation method of A1, calculating semantic feature vectors of the article to be evaluated according to the first neural network model, including:
and obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
A5, classifying the semantic feature vectors according to a second neural network model according to the method for evaluating the quality of an article described in any one of A1-4, including:
classifying the articles to be evaluated according to a third neural network model;
and selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors, and evaluating to obtain the quality of the article to be evaluated.
In a seventh aspect, B6 is an article quality evaluation device, including:
the calculation unit is used for calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and the evaluation unit is used for classifying the semantic feature vectors according to the second neural network model and evaluating the quality of the article to be evaluated.
B7. the quality evaluation device for a sentence according to B6,
the calculation unit is also used for calculating semantic feature vectors of the articles to be trained according to the first neural network model;
and the model training unit is used for carrying out model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
B8, the device for evaluating the quality of the article according to B6, wherein the computing unit comprises:
the acquisition module is used for acquiring semantic feature vectors corresponding to the characters of the article to be evaluated according to the first neural network model;
and the first calculation module is used for calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
B9, the device for evaluating the quality of the article according to B6, wherein the computing unit comprises:
and the second calculation module is used for obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
B10, the apparatus for evaluating the quality of an article according to any one of B6-B9, the evaluation unit comprising:
the classification module is used for classifying the article to be evaluated according to a third neural network model;
and the evaluation module is used for selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors and evaluating the quality of the article to be evaluated.
In an eighth aspect, C11, a storage medium comprising a stored program, wherein the program, when executed, controls a device on which the storage medium is located to execute the method for evaluating the quality of an article according to any one of a1 to a 5.
In a ninth aspect, D12, an apparatus for quality assessment of an article, the apparatus comprising a storage medium; and one or more processors, the storage medium coupled with the processors, the processors configured to execute program instructions stored in the storage medium; the program instructions when executed perform a method of assessing the quality of an article as defined in any one of a1 to a 5.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, embodiments of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present disclosure 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 so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, embodiments of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present disclosure 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 so forth) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for evaluating the quality of an article, comprising:
calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and classifying the semantic feature vectors according to a second neural network model, and evaluating to obtain the quality of the article to be evaluated.
2. A method of quality assessment of an article according to claim 1, wherein prior to classifying said semantic feature vectors according to a second neural network model, said method further comprises:
calculating semantic feature vectors of the article to be trained according to the first neural network model;
and performing model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
3. The article quality evaluation method according to claim 1, wherein calculating semantic feature vectors of the article to be evaluated according to the first neural network model comprises:
obtaining semantic feature vectors corresponding to characters of the article to be evaluated according to the first neural network model;
and calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
4. The article quality evaluation method according to claim 1, wherein calculating semantic feature vectors of the article to be evaluated according to the first neural network model comprises:
and obtaining the weight of each word through the general semantic feature vector and the semantic feature vector of each word of the current sentence, and calculating the semantic feature vector of the article to be evaluated through the weight and the semantic feature vector of each word.
5. A method of assessing the quality of an article according to any one of claims 1 to 4 wherein classifying said semantic feature vectors according to a second neural network model comprises:
classifying the articles to be evaluated according to a third neural network model;
and selecting a second neural network model corresponding to the category of the article to be evaluated, classifying the semantic feature vectors, and evaluating to obtain the quality of the article to be evaluated.
6. An article quality evaluation device, comprising:
the calculation unit is used for calculating semantic feature vectors of the articles to be evaluated according to the first neural network model;
and the evaluation unit is used for classifying the semantic feature vectors according to the second neural network model and evaluating the quality of the article to be evaluated.
7. An article quality evaluation apparatus according to claim 6,
the calculation unit is also used for calculating semantic feature vectors of the articles to be trained according to the first neural network model;
and the model training unit is used for carrying out model training according to the semantic feature vector of the article to be trained and the quality label of the article to be trained to obtain the second neural network model.
8. The article quality evaluation apparatus according to claim 6, wherein the calculation unit includes:
the acquisition module is used for acquiring semantic feature vectors corresponding to the characters of the article to be evaluated according to the first neural network model;
and the first calculation module is used for calculating the semantic feature vector of the article to be evaluated according to the semantic feature vector corresponding to each character in the article to be evaluated.
9. A storage medium comprising a stored program, wherein a device on which the storage medium is located is controlled to execute a method of evaluating the quality of an article according to any one of claims 1 to 5 when the program is run.
10. An apparatus for evaluating the quality of an article, the apparatus comprising a storage medium; and one or more processors, the storage medium coupled with the processors, the processors configured to execute program instructions stored in the storage medium; the program instructions when executed perform a method of assessing the quality of an article as claimed in any one of claims 1 to 5.
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