CN113792230B - Service linking method, device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure provides a service linking method, apparatus, electronic device, and storage medium, and relates to the field of deep learning and natural language processing in the field of artificial intelligence technology. The specific implementation scheme is as follows: acquiring an article; acquiring a target service type to be linked of the article; and linking the service of the target service type to the article according to the target service type and the article. The service linking method, the device, the electronic equipment and the storage medium can meet more requirements generated after the user reads the article by linking different types of services to the article.
Description
Technical Field
The present disclosure relates to the field of deep learning and natural language processing in the field of artificial intelligence technology, and in particular, to a service linking method, apparatus, electronic device, and storage medium.
Background
With the rapid development of the internet, more and more people are used to reading articles through intelligent devices such as smart phones.
At present, the end links of the articles mainly comprise link advertisements and related recommended articles, and cannot meet more requirements generated after users read the articles.
Disclosure of Invention
The disclosure provides a service linking method, a device, an electronic device and a storage medium.
According to a first aspect, there is provided a service linking method, comprising: acquiring an article; acquiring a target service type to be linked of the article; and linking the service of the target service type to the article according to the target service type and the article.
According to a second aspect, there is provided a service linking device comprising: the first acquisition module is used for acquiring the article; the second acquisition module is used for acquiring the target service type to be linked of the article; and the link module is used for linking the service of the target service type to the article according to the target service type and the article.
According to a third aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the service linking method of the first aspect of the present disclosure.
According to a fourth aspect, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the service linking method according to the first aspect of the present disclosure.
According to a fifth aspect, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the service linking method according to the first aspect of the present disclosure.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flow diagram of a service linking method according to a first embodiment of the present disclosure;
fig. 2 is a flow diagram of a service linking method according to a second embodiment of the present disclosure;
fig. 3 is a flow chart of a service linking method according to a third embodiment of the present disclosure;
fig. 4 is a flowchart of a service linking method according to a fourth embodiment of the present disclosure;
Fig. 5 is a flowchart of a service linking method according to a fifth embodiment of the present disclosure;
fig. 6 is a flowchart of a service linking method according to a sixth embodiment of the present disclosure;
fig. 7 is a flowchart of a service linking method according to a seventh embodiment of the present disclosure;
fig. 8 is a flowchart of a service linking method according to an eighth embodiment of the present disclosure;
fig. 9 is a block diagram of a service linking apparatus according to a first embodiment of the present disclosure;
fig. 10 is a block diagram of a service linking apparatus according to a second embodiment of the present disclosure;
fig. 11 is a block diagram of an electronic device for implementing a service linking method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Artificial intelligence (Artificial Intelligence, AI for short) is a piece of technical science that studies, develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. At present, the AI technology has the advantages of high automation degree, high accuracy and low cost, and is widely applied.
Deep Learning (DL) is a new research direction in the field of Machine Learning (ML), and learns the internal rules and presentation layers of sample data, and the information obtained in the Learning process is greatly helpful to the interpretation of data such as text, images and sounds. Its final goal is to have the machine have analytical learning capabilities like a person, and to recognize text, image, and sound data. For the specific research content, the method mainly comprises a neural network system based on convolution operation, namely a convolution neural network; a self-encoding neural network based on a plurality of layers of neurons; and (3) pre-training in a multi-layer self-coding neural network mode, and further optimizing a deep confidence network of the neural network weight by combining the identification information. Deep learning has achieved many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation, and personalization technologies, as well as other related fields. The deep learning makes the machine imitate the activities of human beings such as audio-visual and thinking, solves a plurality of complex pattern recognition problems, and makes the related technology of artificial intelligence greatly advanced.
Natural language processing (Natural Language Processing, NLP) is an important direction in the fields of computer science and artificial intelligence to study computer systems that can effectively implement natural language communications, and in particular, software systems therein.
The following describes a service linking method, apparatus, electronic device, and storage medium of an embodiment of the present disclosure with reference to the accompanying drawings.
Fig. 1 is a flow chart illustrating a service linking method according to a first embodiment of the present disclosure.
As shown in fig. 1, the service linking method according to the embodiment of the disclosure may specifically include the following steps:
s101, acquiring an article.
Specifically, the execution subject of the service linking method according to the embodiments of the present disclosure may be the service linking apparatus provided by the embodiments of the present disclosure, where the service linking apparatus may be a hardware device having data information processing capability and/or software necessary for driving the hardware device to operate. Alternatively, the execution body may include a workstation, a server, a computer, a user terminal, and other devices. The user terminal comprises, but is not limited to, a mobile phone, a computer, intelligent voice interaction equipment, intelligent household appliances, vehicle-mounted terminals and the like.
The initial stage of the image-text landing page contains column information such as advertisements, related recommendations, comments and the like, but lacks related services to meet the extension requirements of users, for example, after a user reads an article for explaining stocks, the user may need to check the real-time trend of the corresponding stocks; after a user reads a travel class article, a reservation for a ticket hotel or the like may be required. Based on this, the embodiment of the disclosure provides a service linking method to link related services to articles, so as to meet more requirements generated after users read the articles.
In the embodiment of the present disclosure, the article to be linked is obtained, and the article is a text description, which may be obtained by an application program, a web page, or the like, and the article types may specifically include, but are not limited to: travel, stock, etc., to which embodiments of the present disclosure do not overly limit. Services may include, but are not limited to: * Health, tachycardia, encyclopedia, stocks, etc.
S102, obtaining the target service type to be linked of the article.
Specifically, the target service type of the article to be linked is determined according to the content of the article. Different service functions are different, single and diversified, page contents (i.e. interfaces) provided by the service are different, and single and diversified, so that different link modes can be selected according to different types of the service, and the accuracy and the effectiveness of the link are ensured. For example, the types of services may include tool-type services and content-type services, wherein the tool-type services may include, but are not limited to: * Healthy, fast, etc., content-based services may include, but are not limited to: * Encyclopedia, stock, etc.
And S103, linking the service of the target service type to the article according to the target service type and the article.
Specifically, for each service type, a service set may be preset, where the set includes a plurality of services of the service type. According to the target service type acquired in the step S102 and the article acquired in the step S101, each service in the service set of the corresponding type is traversed, and the service is linked to the article in a mode matched with the target service type, specifically, can be linked to the end of the article and other positions.
In summary, the service linking method according to the embodiment of the present disclosure obtains an article and a target service type to be linked with, and links a service of the target service type to the article according to the target service type and the article. By linking different types of services to the articles, more needs generated after the user reads the articles can be satisfied.
Fig. 2 is a flow chart of a service linking method according to a second embodiment of the present disclosure.
As shown in fig. 2, on the basis of the embodiment shown in fig. 1, the service linking method of the embodiment of the disclosure specifically may include the following steps:
s201, acquiring an article.
Specifically, step S201 in this embodiment is the same as step S101 in the above embodiment, and will not be described here again.
The step S102 "obtaining the target service type to be linked to the article" may specifically include the following steps S202 to S203:
S202, obtaining candidate service types to be linked of the article.
Specifically, the article of the service to be linked acquired in step S201 is subjected to link judgment, and the service type that the article can link to is obtained as the candidate service type.
S203, determining the target service type according to the click rate of the service of the candidate service type.
Specifically, the candidate service types acquired in step S202 are screened based on the click rate, where the service type with the highest click rate is determined as the target service type.
When the target service type is a tool type service, the step S103 may specifically include the following steps S204 to S206.
S204, identifying interest points of the articles according to the content of the articles.
Specifically, when the target service type is a tool type service, the article may be identified based on a preset algorithm, so as to obtain a plurality of interest points contained in the article. The preset algorithm may be to segment and label the text content of the article, and correspondingly, the text content marked as the interest point obtained after the processing of the preset algorithm is used as the interest point.
S205, obtaining service keywords marked manually by the service.
Specifically, the manual labeling of the keywords is performed on the tool-type service to obtain the service keywords corresponding to the tool-type service.
S206, the service keywords are matched with the interest points of the articles, and the service is linked to the articles.
Specifically, the service keyword of the tool type service acquired in step S205 is matched with the interest point of the article acquired in step S204, and if the service keyword is matched with the interest point of the article, the service is linked to the article. The matching mode may specifically be fuzzy matching, etc., which is not limited in this embodiment of the disclosure.
When the target service type is a content-type service, the above step S103 may specifically include the following steps S207 to S208.
S207, determining the relevance of the article and the page content in the service according to the content of the article.
In particular, when the target service type is a content-type service, a more comprehensive analysis and understanding of the article is required. Page content in service, for example: content services encyclopedia entry in encyclopedia and stock trend in stock, etc. The content of the article includes the title of the article and the body of the article. And determining the relevance of the article and the page content in the service according to the content of the article.
And S208, linking the page content in the service to the article according to the relevance.
Specifically, if the relevance determined in step S207 meets the link relevance requirement, the page content in the service is linked to the article, and if the relevance determined in step S207 does not meet the link relevance requirement, the page content in the service is not linked to the article.
As a first possible implementation manner, when the content-type service is an encyclopedia service, for example, encyclopedia, as shown in fig. 3, the step S207 "determining the relevance between the article and the page content in the service according to the content of the article" may specifically include the following steps S301 to S303:
s301, acquiring a preset number of entity words with highest importance in the articles according to the content of the articles.
Specifically, a plurality of entity words in the article and the importance degree corresponding to each entity word are obtained according to the content of the article, the entity words are arranged in the order from high importance degree to low importance degree, and the preset number of entity words which are ranked at the front are determined as the preset number of entity words with the highest importance degree in the article. The preset number may be preset as needed, for example, 10. Entity words include nouns and pronouns, such as characters, things, and the like.
S302, obtaining encyclopedic entry corresponding to the entity word according to the title of the article.
Specifically, the encyclopedic entry corresponding to the entity word is obtained, and for the encyclopedic entry with a plurality of paraphraseology, the paraphraseology with the highest accuracy rate can be screened from the plurality of paraphrased encyclopedic entries according to the title of the article, for example, a text classification convolutional neural network (TextCNN) model can be adopted for screening.
And S303, determining the correlation between the encyclopedia entry and the text of the article.
In particular, the correlation between the encyclopedia entry and the body of the article may be determined in at least one of the following ways to avoid mislinking: 1) Whether the entity word is matched with a non-subject word in the title of the article is determined, wherein the non-subject word in the title of the article is determined according to the text of the article, for example, the title of the article is 'A scenery spot which can be compared with a West lake, people do not know the same', the text of the article mainly introduces the A scenery spot, and therefore the semantic model of the article recognizes 'West lake' as the non-subject word. If the entity word is matched with the non-subject word in the title of the article, determining that the correlation between the encyclopedia entry and the text of the article does not meet the requirement; 2) Determining the occurrence times of the entity words in the text of the article, and if the occurrence times of the entity words in the text of the article are smaller than a preset time threshold, determining that the correlation between the encyclopedic vocabulary entry and the text of the article does not meet the requirement; 3) And determining the ratio of the number of paragraphs of the entity word appearing in the text of the article to the total number of paragraphs, and if the ratio is smaller than a preset ratio threshold, determining that the correlation between the encyclopedic entry and the text of the article is not satisfactory.
Correspondingly, the step S208 "link in-service page content to an article according to relevance" may specifically include the following step S304.
S304, linking encyclopedia entries to the articles according to the relevance.
Specifically, encyclopedia entries meeting relevance requirements are linked to articles.
Further, as shown in fig. 4, the step S301 "obtaining the preset number of entity words" with the highest importance in the article according to the content of the article "may specifically include the following steps S401 to S403.
S401, acquiring a plurality of entity words in the content of the article.
Specifically, the title and the text of the article are respectively segmented, and entity words in the article are identified. The identification method specifically may include, but is not limited to: a recognition method based on a Chinese language processing package (Han Language Processing, abbreviated as Hanlp), a recognition method based on a Stanford university core natural language processing package (Stanford core Natural LanguageProcessing, abbreviated as Stanfordcorenp), a recognition method based on a language technology platform (Language Technology Platform, abbreviated as Ltp), a recognition method based on a Bidirectional Long-Short-Term Memory-cyclic neural network-conditional random field (BI-directional-Long-Short-Term Memory-Recurrent Neural Network-Conditional Random Fields, abbreviated as BI_LSTM_RNN_CRF) and the like.
S402, inputting the texts of the plurality of entity words and the articles into a gradient lifting decision tree model to obtain importance degrees corresponding to the plurality of entity words.
Specifically, the text of the article and the plurality of entity words obtained in step S401 may be input into a gradient boost decision tree (Gradient Boosting Decision Tree, GBDT for short) model, and the GBDT model outputs the importance degrees corresponding to the plurality of entity words.
S403, determining a preset number of entity words with highest importance according to the importance degrees corresponding to the entity words.
Specifically, the plurality of entity words are arranged according to the order of the importance level from high to low, and the preset number of entity words with the top ranking is determined as the preset number of entity words with the highest importance level in the article.
As a second possible implementation manner, when the content-type service is a stock service, for example, stock, as shown in fig. 5, the step S207 "determining the relevance between the article and the page content in the service according to the content of the article" may specifically include the following steps S501-S502:
s501, acquiring the stock entity word with the highest weight in the article according to the content of the article, wherein the weight of the stock entity word is determined according to the number of times of occurrence of the stock entity word in the article and the occurrence position.
Specifically, a plurality of entity words in the article are obtained according to the content of the article, the entity words related to stocks are screened out to serve as stock entity words, the weight of the stock entity words is determined according to the number of times of occurrence of the stock entity words in the article and the occurrence position, for example, the more the number of times is, the higher the corresponding weight is, and the more important the title appearing in the article is than the weight appearing in the text of the article. And arranging the plurality of stock entity words in the order from high to low in weight, and determining the stock entity word with the highest ranking as the stock entity word with the highest weight in the article.
S502, determining the correlation between the stock entity words and the articles.
Specifically, after obtaining the stock entity word, it can determine whether the article can link stocks, so as to ensure the link accuracy. Specifically, the correlation between the stock entity word and the article can be determined according to the number of times that the stock keyword in the preset stock keyword set appears in the article. Wherein the stock keyword set may include, but is not limited to: stock keywords such as warehouse adding, stock, rising, falling, market value, merging, financial reporting, marketing, evidence index, profit, income, net income annual reporting, depletion, sales and the like. And when the occurrence number exceeds a preset number threshold, determining to meet the correlation requirement, namely that the article can link stocks.
Correspondingly, the step S208 "link in-service page content to an article according to relevance" may specifically include the following step S503.
And S503, linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
Specifically, stock trends corresponding to the stock entity words meeting the relevance requirements are linked to the articles.
Further, as shown in fig. 6, the step S501 of "acquiring the highest weighted stock entity word in the article according to the content of the article" may specifically include the following steps S601-S603:
s601, acquiring a plurality of entity words in the content of the article.
Specifically, step S601 in this embodiment is the same as step S401 in the above embodiment, and will not be described here again.
S602, determining entity words consistent with preset stock names in the entity words as stock entity words.
Specifically, names and names corresponding to all stocks recorded in the stock full-quantity synonym dictionary can be determined as preset stock names. And comparing the plurality of entity words with preset stock names, and determining the entity words consistent with the preset stock names as stock entity words.
S603, determining the stock entity word with the highest weight according to the weight of the stock entity word.
Specifically, the weight of the stock entity word is determined according to the number of times the stock entity word appears in the article and the position of the occurrence, for example, the more the number of times is, the higher the corresponding weight is, and the more important the title appearing in the article is than the weight appearing in the body of the article. And arranging the plurality of stock entity words in the order from high to low in weight, and determining the stock entity word with the highest ranking as the stock entity word with the highest weight in the article.
Further, as shown in fig. 7, the step S503 "link the stock trends corresponding to the stock entity words to the articles according to the relevance" may specifically include the following steps S701-S702.
S701, performing stock link evaluation on the article.
Specifically, stock link evaluation is performed on the articles to determine whether the articles are suitable for linking stocks, so as to ensure the link accuracy and effectiveness. The determination may be made, for example, using a pre-trained bert model. Firstly, manually labeling a batch of sample articles, judging whether the sample articles are suitable for stock linking, then training a classification model, wherein the input of the classification model is the first 512 Chinese characters of the title and the text of the sample articles, after each Chinese character is subjected to ID, inputting a series of sequences, and the classification model outputs a score, and the higher the score, the higher the possibility that the articles are suitable for stock linking.
S702, if the evaluation passes, the stock trends corresponding to the stock entity words are linked to the articles according to the relevance.
Specifically, if the evaluation passes, for example, if the score output by the pre-training bert model exceeds a preset score threshold, the evaluation passes, the stock trends corresponding to the stock entity words are linked to the articles according to the relevance.
In summary, according to the service linking method of the embodiment of the present disclosure, an article and a candidate service type to be linked are obtained, and a target service type is determined according to a click rate of a service of the candidate service type. For a tool-type service, if the service keyword matches the point of interest of the article, then it is linked. For content-type services, links are made according to the relevance of the articles to the content of the pages in the service. By linking different types of services to the articles, more needs generated after the user reads the articles can be satisfied. Different modes are adopted for linking aiming at different types of services, so that the accuracy and the effectiveness of the linking can be ensured. For encyclopedia service, after the encyclopedia entry corresponding to the entity word is acquired, the correlation between the encyclopedia entry and the text of the article is further determined, so that incorrect linking can be avoided. For the stock service, after obtaining the stock entity word, further carrying out correlation judgment on whether the article can link stocks or not and evaluating whether the article is suitable for linking stocks or not so as to ensure the accuracy and the effectiveness of the linking.
In order to clearly illustrate the service linking method of the embodiment of the present disclosure, a detailed description is given below of a specific procedure of the service linking method of the embodiment of the present disclosure by way of fig. 8. As shown in fig. 8, the service linking method according to the embodiment of the disclosure may specifically include the following steps:
s801, acquiring an article.
S802, obtaining candidate service types to be linked of the article.
S803, determining the target service type according to the click rate of the service of the candidate service type.
When the target service type is the tool type service, steps S804 to S806 are continued.
S804, identifying interest points of the articles according to the contents of the articles.
S805, obtaining service keywords of the service manual annotation.
And S806, if the service keywords are matched with the interest points of the articles, linking the service to the articles.
When the target service type is a content-encyclopedia service, steps S807-S812 are continued.
S807, a plurality of entity words in the content of the article are acquired.
S808, inputting the texts of the plurality of entity words and the articles into the gradient lifting decision tree model to obtain the importance degrees corresponding to the plurality of entity words.
S809, determining a preset number of entity words with highest importance according to the importance degrees corresponding to the entity words.
S810, acquiring encyclopedic entry corresponding to the entity word according to the title of the article.
S811, determining the correlation between the encyclopedia entry and the text of the article.
And S812, linking the encyclopedia entry to the article according to the relevance.
When the target service type is the content service-stock service, steps S813 to S818 are continued.
S813, a plurality of entity words in the content of the article are acquired.
S814, determining the entity word which is consistent with the preset stock name from the entity words as the stock entity word.
S815, determining the stock entity word with the highest weight according to the weight of the stock entity word.
S816, a correlation between the stock entity word and the article is determined.
S817, the article is subjected to stock link assessment.
And S818, if the evaluation passes, linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
Fig. 9 is a block diagram of a service linking apparatus according to a first embodiment of the present disclosure.
As shown in fig. 9, a service linking apparatus 900 of an embodiment of the present disclosure includes: a first acquisition module 901, a second acquisition module 902, and a linking module 903.
A first obtaining module 901, configured to obtain an article.
And a second obtaining module 902, configured to obtain a target service type to be linked to the article.
And the linking module 903 is configured to link the service of the target service type to the article according to the target service type and the article.
It should be noted that the above explanation of the service linking method embodiment is also applicable to the service linking device of the embodiment of the present disclosure, and the specific process is not repeated here.
In summary, the service linking device of the embodiment of the present disclosure obtains an article and a target service type to be linked with, and links a service of the target service type to the article according to the target service type and the article. By linking different types of services to the articles, more needs generated after the user reads the articles can be satisfied.
Fig. 10 is a block diagram of a service linking apparatus according to a second embodiment of the present disclosure.
As shown in fig. 10, a service linking apparatus 1000 of an embodiment of the present disclosure includes: a first acquisition module 1001, a second acquisition module 1002, and a linking module 1003.
The first acquisition module 1001 has the same structure and function as the first acquisition module 901 in the previous embodiment, and the second acquisition module 1002 has the same structure and function as the second acquisition module 902 in the previous embodiment. The link module 1003 has the same structure and function as the link module 903 in the previous embodiment.
Further, the second obtaining module 1002 includes: a first obtaining submodule 10021, configured to obtain a candidate service type to be linked to the article; a first determining submodule 10022 is configured to determine the target service type according to the click rate of the service of the candidate service type.
Further, the service type is a tool type service, and the link module 1003 includes: the identifying sub-module is used for identifying interest points of the article according to the content of the article; the second acquisition sub-module is used for acquiring the service keywords marked by the service personnel; and the first link sub-module is used for linking the service to the article if the service keyword is matched with the interest point of the article.
Further, the service type is a content type service, and the link module 1003 includes: a second determining submodule, configured to determine, according to the content of the article, a relevance between the article and the content of the page in the service; and a second linking sub-module for linking the page content in the service to the article according to the relevance.
Further, the service is an encyclopedia service, and the second determining submodule includes: the first acquisition unit is used for acquiring a preset number of entity words with highest importance in the article according to the content of the article; a second obtaining unit, configured to obtain encyclopedic entry corresponding to the entity word according to the title of the article; and a first determining unit for determining the correlation between the encyclopedia entry and the body of the article; the second link sub-module includes: and a first linking unit for linking the encyclopedia entry to the article according to the relevance.
Further, the first acquisition unit includes: a first obtaining subunit, configured to obtain a plurality of entity words in the content of the article; an input subunit, configured to input the plurality of entity words and the text of the article to a gradient-enhanced decision tree model, so as to obtain importance degrees corresponding to the plurality of entity words; and the first determining subunit is used for determining a preset number of entity words with highest importance according to the importance corresponding to the entity words.
Further, the determining unit includes at least one subunit of: a second determining subunit, configured to determine whether the entity word matches a non-subject word in a title of the article, where the non-subject word in the title of the article is determined according to a body of the article; a third determining subunit, configured to determine a number of times the entity word appears in the text of the article; and a fourth determining subunit, configured to determine a ratio of a number of paragraphs in the text of the article in which the entity word appears to a total number of paragraphs.
Further, the service is a stock service, and the second determining submodule includes: a third obtaining unit, configured to obtain, according to the content of the article, a stock entity word with the highest weight in the article, where the weight of the stock entity word is determined according to the number of occurrences of the stock entity word in the article and the location of the occurrence; and a second determining unit configured to determine the correlation between the stock entity word and the article; the second link sub-module includes: and the second link unit is used for linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
Further, the third obtaining unit includes: a second obtaining subunit, configured to obtain a plurality of entity words in the content of the article; a fifth determining subunit, configured to determine, as the stock entity word, an entity word that is consistent with a preset stock name from the plurality of entity words; and a sixth determining subunit, configured to determine, according to the weight of the stock entity word, the stock entity word with the highest weight.
Further, the second determining unit includes: and a seventh determining subunit, configured to determine the correlation between the stock entity word and the article according to the number of times that the stock keyword in the preset stock keyword set appears in the article.
Further, the second linking unit includes: an evaluation subunit, configured to perform stock link evaluation on the article; and the link subunit is used for linking the stock trends corresponding to the stock entity words to the articles according to the relevance when the evaluation passes.
It should be noted that the above explanation of the service linking method embodiment is also applicable to the service linking device of the embodiment of the present disclosure, and the specific process is not repeated here.
In summary, the service linking device in the embodiment of the present disclosure obtains an article and a candidate service type to be linked with, and determines a target service type according to a click rate of a service of the candidate service type. For a tool-type service, if the service keyword matches the point of interest of the article, then it is linked. For content-type services, links are made according to the relevance of the articles to the content of the pages in the service. By linking different types of services to the articles, more needs generated after the user reads the articles can be satisfied. Different modes are adopted for linking aiming at different types of services, so that the accuracy and the effectiveness of the linking can be ensured. For encyclopedia service, after the encyclopedia entry corresponding to the entity word is acquired, the correlation between the encyclopedia entry and the text of the article is further determined, so that incorrect linking can be avoided. For the stock service, after obtaining the stock entity word, further carrying out correlation judgment on whether the article can link stocks or not and evaluating whether the article is suitable for linking stocks or not so as to ensure the accuracy and the effectiveness of the linking.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 11 illustrates a schematic block diagram of an example electronic device 1100 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the electronic device 1100 includes a computing unit 1101 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, ROM 1102, and RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.
A number of components in the electronic device 1100 are connected to the I/O interface 1105, including: an input unit 1106 such as a keyboard, a mouse, etc.; an output unit 1107 such as various types of displays, speakers, and the like; a storage unit 1108, such as a magnetic disk, optical disk, etc.; and a communication unit 1109 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 1109 allows the electronic device 1100 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks.
The computing unit 1101 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the respective methods and processes described above, such as the service linking method shown in fig. 1 to 8. For example, in some embodiments, the service linking method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1108. In some embodiments, some or all of the computer programs may be loaded and/or installed onto electronic device 1100 via ROM 1102 and/or communication unit 1109. When the computer program is loaded into the RAM 1103 and executed by the computing unit 1101, one or more steps of the service linking method described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to perform the service linking method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
According to an embodiment of the present disclosure, the present disclosure further provides a computer program product comprising a computer program, wherein the computer program, when being executed by a processor, implements the steps of the service linking method according to the above-described embodiments of the present disclosure.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (18)
1. A service linking method, comprising:
acquiring an article;
acquiring a target service type to be linked of the article; and
linking a service of the target service type to the article according to the target service type and the article;
The obtaining the target service type to be linked of the article includes:
acquiring candidate service types to be linked of the article; and
determining the target service type according to the click rate of the service of the candidate service type;
wherein the target service type is a tool type service, and the linking the service of the target service type to the article according to the target service type and the article includes:
identifying interest points of the article according to the content of the article;
acquiring service keywords of the service manual annotation; and
the service keywords are matched with the interest points of the articles, and then the service is linked to the articles;
wherein the target service type is a content type service, and the linking the service of the target service type to the article according to the target service type and the article includes:
determining the relevance of the article and the page content in the service according to the content of the article; and
and linking the page content in the service to the article according to the relevance.
2. The service linking method according to claim 1, wherein the service is an encyclopedia service, the determining relevance of the article to the in-service page content according to the content of the article includes:
Acquiring a preset number of entity words with highest importance in the article according to the content of the article;
acquiring encyclopedic entry corresponding to the entity word according to the title of the article; and
determining the relevance between the encyclopedia entry and the body of the article;
the linking the page content in the service to the article according to the relevance includes:
the encyclopedia entry is linked to the article according to the relevance.
3. The service linking method according to claim 2, wherein the obtaining, according to the content of the article, a preset number of entity words with highest importance in the article includes:
acquiring a plurality of entity words in the content of the article;
inputting the entity words and the text of the article into a gradient lifting decision tree model to obtain importance degrees corresponding to the entity words; and
and determining the preset number of entity words with the highest importance according to the importance corresponding to the plurality of entity words.
4. The service linking method of claim 2, wherein the determining the relevance between the encyclopedia entry and the body of the article comprises at least one of:
Determining whether the entity word is matched with a non-subject word in the title of the article, wherein the non-subject word in the title of the article is determined according to the text of the article;
determining the number of times the entity word appears in the text of the article; and
and determining the ratio of the number of paragraphs of the entity word appearing in the text of the article to the total number of paragraphs.
5. The service linking method according to claim 1, wherein the service is a stock service, and the determining the relevance of the article to the page content in the service according to the content of the article includes:
acquiring a stock entity word with highest weight in the article according to the content of the article, wherein the weight of the stock entity word is determined according to the occurrence frequency and the occurrence position of the stock entity word in the article; and
determining the relevance between the stock entity word and the article;
the linking the page content in the service to the article according to the relevance includes:
and linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
6. The service linking method as claimed in claim 5, wherein the acquiring the highest weighted stock entity word in the article according to the content of the article comprises:
Acquiring a plurality of entity words in the content of the article;
determining entity words consistent with a preset stock name in the entity words as stock entity words; and
and determining the stock entity word with the highest weight according to the weight of the stock entity word.
7. The service linking method of claim 5, wherein the determining the relevance between the stock entity word and the article comprises:
and determining the correlation between the stock entity word and the article according to the frequency of occurrence of the stock keyword in the preset stock keyword set in the article.
8. The service linking method according to claim 5, the linking the stock trends corresponding to the stock entity words to the articles according to the relevance, comprising:
performing stock link evaluation on the article;
and if the evaluation is passed, linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
9. A service linking apparatus comprising:
the first acquisition module is used for acquiring the article;
the second acquisition module is used for acquiring the target service type to be linked of the article; and
The link module is used for linking the service of the target service type to the article according to the target service type and the article;
wherein, the second acquisition module includes:
the first acquisition sub-module is used for acquiring candidate service types to be linked of the article;
the first determining submodule is used for determining the target service type according to the click rate of the service of the candidate service type;
wherein the target service type is a tool type service, and the link module includes:
the identifying sub-module is used for identifying interest points of the article according to the content of the article;
the second acquisition sub-module is used for acquiring the service keywords marked by the service personnel; and
the first link sub-module is used for linking the service to the article if the service keyword is matched with the interest point of the article;
wherein the target service type is content type service, and the link module comprises:
a second determining submodule, configured to determine, according to the content of the article, a relevance between the article and the content of the page in the service; and
and the second link sub-module is used for linking the page content in the service to the article according to the relevance.
10. The service linking device of claim 9, wherein the service is an encyclopedia service, the second determination submodule comprising:
the first acquisition unit is used for acquiring a preset number of entity words with highest importance in the article according to the content of the article;
a second obtaining unit, configured to obtain encyclopedic entry corresponding to the entity word according to the title of the article; and
a first determining unit configured to determine the correlation between the encyclopedia entry and the body of the article;
the second link sub-module includes:
and a first linking unit for linking the encyclopedia entry to the article according to the relevance.
11. The service linking device according to claim 10, wherein the first acquisition unit includes:
a first obtaining subunit, configured to obtain a plurality of entity words in the content of the article;
an input subunit, configured to input the plurality of entity words and the text of the article to a gradient-enhanced decision tree model, so as to obtain importance degrees corresponding to the plurality of entity words; and
and the first determining subunit is used for determining a preset number of entity words with highest importance according to the importance corresponding to the plurality of entity words.
12. The service linking device of claim 10, wherein the determining unit comprises at least one subunit of:
a second determining subunit, configured to determine whether the entity word matches a non-subject word in a title of the article, where the non-subject word in the title of the article is determined according to a body of the article;
a third determining subunit, configured to determine a number of times the entity word appears in the text of the article; and
and a fourth determining subunit, configured to determine a ratio of a number of paragraphs in the text of the article in which the entity word appears to a total number of paragraphs.
13. The service linking device of claim 9, wherein the service is a stock service, and the second determination submodule includes:
a third obtaining unit, configured to obtain, according to the content of the article, a stock entity word with the highest weight in the article, where the weight of the stock entity word is determined according to the number of occurrences of the stock entity word in the article and the location of the occurrence; and
a second determining unit configured to determine the correlation between the stock entity word and the article;
the second link sub-module includes:
And the second link unit is used for linking the stock trends corresponding to the stock entity words to the articles according to the relevance.
14. The service linking device according to claim 13, wherein the third acquisition unit includes:
a second obtaining subunit, configured to obtain a plurality of entity words in the content of the article;
a fifth determining subunit, configured to determine, as the stock entity word, an entity word that is consistent with a preset stock name from the plurality of entity words; and
and a sixth determining subunit, configured to determine, according to the weight of the stock entity word, the stock entity word with the highest weight.
15. The service linking device according to claim 13, wherein the second determining unit includes:
and a seventh determining subunit, configured to determine the correlation between the stock entity word and the article according to the number of times that the stock keyword in the preset stock keyword set appears in the article.
16. The service linking device according to claim 13, the second linking unit comprising:
an evaluation subunit, configured to perform stock link evaluation on the article;
and the link subunit is used for linking the stock trends corresponding to the stock entity words to the articles according to the relevance when the evaluation passes.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.
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