CN112699314A - Hot event determination method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a method and a device for determining a hot event, electronic equipment, a storage medium and a computer program product, and relates to the technical field of internet and the technical field of information mining and searching. The specific implementation scheme is as follows: capturing hot webpage resources; performing event semantic understanding on hot webpage resources to obtain a plurality of original events; performing semantic correlation matching on the target search word and a plurality of original events to generate event dimension characteristics; and screening out hot events from the plurality of original events according to the event dimension characteristics and the searching frequency characteristics of the target searching words. According to the technical scheme, the efficiency and the accuracy of identifying the hot spot events of the whole network can be improved.
Description
Technical Field
The application relates to the technical field of internet, in particular to the technical field of information mining and searching.
Background
The operation of the hot event is always the object of the key operation of the information application. A whole network can have a plurality of events every day, and the number of events really reaching the operation level is not large. The hot spot event is mined manually, and whether the current event is the hot spot event or not is judged according to experience, so that the cost is high and the efficiency is low.
Disclosure of Invention
The application provides a hotspot event determination method and device, electronic equipment, storage medium and computer program product.
According to an aspect of the present application, there is provided a hotspot event determination method, including:
capturing hot webpage resources;
performing event semantic understanding on hot webpage resources to obtain a plurality of original events;
performing semantic correlation matching on the target search word and a plurality of original events to generate event dimension characteristics;
and screening out hot events from the plurality of original events according to the event dimension characteristics and the searching frequency characteristics of the target searching words.
According to another aspect of the present application, there is provided a hotspot event determination device, including:
the capturing unit is used for capturing hot webpage resources;
the event extraction unit is used for performing event semantic understanding on hot webpage resources to obtain a plurality of original events;
the feature extraction unit is used for performing semantic relevance matching on the target search word and a plurality of original events to generate event dimension features;
the first determining unit is used for screening out hot events from a plurality of original events according to the event dimension characteristics and the searching frequency characteristics of the target searching words.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of the embodiments of the present application.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method in any of the embodiments of the present application.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present application.
According to the technical scheme, the efficiency and the accuracy of identifying the hot spot events of the whole network can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a first schematic diagram of a hot spot event determination method according to an embodiment of the present application;
FIG. 2 is a second schematic diagram of a hot spot event determination method according to an embodiment of the present application;
FIG. 3 is a third schematic diagram of a hot spot event determination method according to an embodiment of the present application;
FIG. 4 is a fourth schematic diagram of a hot spot event determination method according to an embodiment of the present application;
FIG. 5 is a fifth schematic diagram of a hot spot event determination method according to an embodiment of the present application;
fig. 6 is a schematic diagram of an architecture for operating a hotspot event in an embodiment of the present application;
FIG. 7 is a schematic diagram of a hotspot event determination device according to one embodiment of the present application;
fig. 8 is a schematic diagram of a hot spot event determination apparatus according to yet another embodiment of the present application;
fig. 9 is a block diagram of an electronic device for implementing a hot event determination method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram illustrating a hotspot event determination method according to an embodiment of the present application, which may be applied to an electronic device including, but not limited to, a fixed device including, but not limited to, a server, which may be a cloud server or a general server, and/or a mobile device. For example, mobile devices include, but are not limited to: one or more of a cell phone or a tablet computer. As shown in fig. 1, the method includes:
step S11, capturing hot web page resources;
step S12, performing event semantic understanding on hot webpage resources to obtain a plurality of original events;
step S13, semantic relevance matching is carried out on the target search word and a plurality of original events to generate event dimension characteristics;
and step S14, screening out hot events from a plurality of original events according to the event dimension characteristics and the searching frequency characteristics of the target search terms.
The hot webpage resources are obtained by crawling the whole network information website by using a crawler, so that the complete sudden hot webpage can be obtained at the first time.
Wherein the original event is an event to be further screened or confirmed. In the plurality of original events in step S12, some original events may not satisfy the subsequent screening condition, some original events may satisfy the subsequent screening condition, and those original events that satisfy the subsequent screening condition are determined as hot events.
The target search term in step S13 is a search term with a search frequency exceeding a preset threshold. Because the search times of different search terms may be different, for example, the search time corresponding to the search term a in a preset time period is i, and the search time corresponding to the search term B is j, where the value of i is a positive integer, and the value of j is a positive integer, and if i is greater than a preset threshold, the search term a is regarded as a target search term; if j is less than the preset threshold, the search term B is not considered as the target search term. In practical applications, the preset threshold may be set or adjusted according to requirements.
The target search term (also referred to as query) in step S13 is information input by the user on the terminal device. Illustratively, the target search term may contain one or more of the following: person name, item name, time, location, keyword. For example, if the user enters "Liu De Hua", the search term is "Liu De Hua". For another example, if the user inputs "celebration eleven," the search term is "celebration eleven.
The event can have multiple dimensions, the event dimension feature is a feature used for representing the dimensions of the event, the number of the dimensions of the event is not limited, and the specific number can be set or adjusted according to design requirements. For example, the event dimension features include one or more of the following features: core words of the current event, primary summary, site weight, and how many relevant sites of the full network outbreak are. Here, the site refers to a website.
The search frequency characteristic is a characteristic used for representing search behaviors of the user. For example, the search frequency characteristics include one or more of the following characteristics: the frequency of searching words searched by the user and the correlation coefficient between the event correlation and the searching words searched by the user.
Therefore, according to the scheme of the application, the captured hot webpage resources are subjected to event semantic understanding to obtain a plurality of original events, and the original events which are possible to become the hot events can be quickly screened out from the whole network preliminarily; generating event dimension characteristics by performing semantic relevance matching on a target search word and a plurality of original events; according to the event dimension characteristics and the search frequency characteristics of the target search terms, hot events are screened from a plurality of original events, the preliminarily screened original events can be further screened to obtain the hot events, and therefore the hot events of the whole network can be automatically identified; compared with a mode of manually excavating hot events, the method and the system have the advantages that the excavating range is wider, and the excavating speed is higher; and hot events are screened by combining the search frequency characteristics and the event dimension characteristics, so that the efficiency and the accuracy of identifying the hot events in the whole network can be improved.
In this embodiment of the application, on the basis of implementing any one of the methods described above, as shown in fig. 2, the method may further include:
step S15, according to the indication information of whether to operate the hot event, the target hot event is determined.
The target hotspot event refers to a hotspot event to be operated.
In some embodiments, the electronic device pushes the hot event screened in step S15 to a terminal where an operator is located, and the operator manually screens the hot event to determine whether to operate the hot event; and the electronic equipment further screens out the target hotspot event from the plurality of hotspot events according to the indication information sent by the terminal where the operator is located.
Through the embodiment, the operation efficiency and accuracy of the hotspot event can be improved. Because operators do not need to manually mine hot events, the target hot event to be operated is selected from the received hot events according to the characteristics of event timeliness, heat and the like, the working efficiency can be improved, and the labor cost is reduced.
In this embodiment of the application, on the basis of implementing any one of the methods described above, as shown in fig. 3, the method may further include:
and step S16, excavating the material aiming at the target hot event to obtain the material resource corresponding to the target hot event.
Wherein, material refers to material required for operating a target hot spot event. For example, the material includes one or more of the following: an article; a picture; video; audio frequency; the link address.
In some embodiments, material mining is performed for the target hotspot event, including one or more of:
performing full-network high-quality information mining on the target hotspot event;
carrying out full-network picture mining aiming at the target hotspot event;
carrying out whole-network video mining aiming at the target hotspot event;
and carrying out whole-network audio mining on the target hotspot event.
Through above-mentioned embodiment, can carry out online material screening automatically, realize that the incident material excavates in real time to be convenient for the operation personnel to find the resource of operation service demand fast, promote operation efficiency.
In the embodiment of the present application, on the basis of implementing any one of the methods described above, as shown in fig. 4, the method may further include the following steps:
step S17, a target semantic model for event semantic understanding is obtained, wherein the target semantic model is obtained after a preset semantic model is trained based on a first training sample.
In some embodiments, performing event semantic understanding on the hot web page resource to obtain a plurality of original events, including:
and inputting the hot webpage resources into the target semantic model to obtain a plurality of original events output by the target semantic model.
Here, the preset semantic model may employ various pre-training language models, such as a converter-based Bidirectional Encoding Representation (BERT) model, a kNowledge Enhanced semantic Representation (ERNIE) model, and the like.
It should be noted that, the present application does not limit the training mode and the training process of the preset semantic model.
Through the implementation mode, the target semantic model can be obtained, the captured hot webpage resources are input into the target semantic model, the original events can be automatically output, and compared with the method of screening the original events in a manual mode, the method and the device for screening the original events can improve the efficiency and accuracy of screening the original events, and therefore the method and the device for identifying the hot events are beneficial to improving the efficiency and accuracy of identifying the hot events.
In the embodiment of the present application, on the basis of implementing any one of the methods described above, as shown in fig. 5, before step S11, the method further includes:
step S18, obtaining a target event model, wherein the target event model is obtained after a preset event model is trained based on a second training sample, and is used for screening hot events according to the event dimension characteristics and the search frequency characteristics.
It should be noted that the second training sample may be the same training sample as the first training sample mentioned above, or may be a different training sample.
In some embodiments, screening out hot events from a plurality of original events according to the event dimension characteristics and the search frequency characteristics of the target search term includes:
inputting the event dimension characteristics and the search frequency characteristics of the target search words into a target event model to obtain a hot event which is output by the target event model and screened from a plurality of original events.
Here, the preset event model may adopt various machine learning algorithm models, such as a Gradient Boosting Decision Tree (GBDT) model, an eXtreme Gradient Boosting (XGBoost) model, and the like.
In practical application, the hot events screened by the target event model can be output in a list form, for example, a hot event list is generated at intervals, so that an operator can determine whether to operate the current hot event according to the characteristics of event timeliness, heat degree and the like.
Through the embodiment, the target event model can be obtained, the search frequency characteristics and the original events are input into the target event model, the hot events can be automatically output, and compared with the method of mining the hot events in an artificial mode, the efficiency and accuracy of mining the hot events can be improved, so that the efficiency and accuracy of identifying the hot events are improved.
In this embodiment of the present application, on the basis of implementing any one of the above methods, training a preset event model based on a second training sample includes:
and training a preset event model based on the sample event dimension characteristic and the sample search frequency characteristic corresponding to the second training sample.
In the above embodiment, the sample search frequency characteristic and the sample event dimension characteristic are considered when the preset event model is trained, so that the accuracy of the target event model obtained by training outputting the hot event can be improved.
FIG. 6 shows a schematic diagram of an architecture of an operation hotspot event, and as can be seen from FIG. 6, a full-network information stream is input into a target semantic model, event generation is performed by the target semantic model, an original event is output to an original event library, semantic association is performed on the original event in the original event library according to an online real-time high-frequency search word, and an event dimension feature and a search frequency feature are obtained; inputting the event dimension characteristics and the search frequency characteristics into a target event model, scoring the original events by the target event model, and determining hot events according to scoring results, for example, taking the original events with the scoring values ranked M before as the hot events; screening out a target hotspot event to be operated from the hotspot events by operators; and carrying out material mining based on the hot event to obtain a complete target hot event to be operated. Wherein, the target semantic model can adopt a BERT model, and the target event model can adopt a GBDT model. Therefore, the full-network hot spot can be automatically identified through the framework, and the high-quality material corresponding to the hot spot event is mined. Operators can position the hot spot more quickly through the framework, operation is completed, and hot spot operation efficiency is improved.
It should be understood that the architecture shown in fig. 6 is an alternative specific implementation, and that various obvious changes and/or substitutions may be made by those skilled in the art based on the example of fig. 6, and still fall within the scope of the disclosure of the embodiments of the disclosure.
The hot event determining method provided by the application can be used for items such as search engines or search recommendations. Illustratively, the execution subject of the method may be an electronic device, which may be a variety of search engine devices, such as a search engine server.
As an implementation of the foregoing methods, the present application also provides a device for determining a hotspot event. Fig. 7 shows a schematic diagram of a hot spot event determining apparatus. As shown in fig. 7, the apparatus includes:
a crawling unit 710, configured to crawl hot web page resources;
the event extraction unit 720 is configured to perform event semantic understanding on the hot web page resource to obtain a plurality of original events;
the feature extraction unit 730 is configured to perform semantic relevance matching on the target search term and the plurality of original events to generate event dimension features;
the first determining unit 740 is configured to screen out a hot event from the multiple original events according to the event dimension feature and the search frequency feature of the target search term.
In some embodiments, as shown in fig. 8, the apparatus may further include:
a second determining unit 750, configured to determine a target hotspot event according to the indication information of whether to operate the hotspot event.
In some embodiments, as shown in fig. 8, the apparatus may further include:
the material mining unit 760 is configured to mine the material for the target hot event to obtain a material resource corresponding to the target hot event.
In some embodiments, as shown in fig. 8, the apparatus may further include:
a first obtaining unit 770, configured to obtain a target semantic model for performing event semantic understanding, where the target semantic model is obtained by training a preset semantic model based on a first training sample;
the event extraction unit 730 is configured to:
and inputting the hot webpage resources into the target semantic model to obtain a plurality of original events output by the target semantic model.
In some embodiments, as shown in fig. 8, the apparatus may further include:
a second obtaining unit 780, configured to obtain a target event model, where the target event model is obtained after a preset event model is trained based on a second training sample, and is used to screen a hot event according to an event dimension feature and a search frequency feature;
the first determining unit 740 is configured to:
inputting the event dimension characteristic and the search frequency characteristic of the target search term into the target event model to obtain the hot events which are output by the target event model and screened from the plurality of original events.
And the target event model is obtained by training a preset event model based on the sample event dimension characteristic and the sample search frequency characteristic corresponding to the second training sample.
The device for determining the hot event can automatically mine the hot event, and can improve the efficiency and accuracy of mining the hot event compared with the method of mining the hot event manually, so that the efficiency and accuracy of operating the hot event are improved.
There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product.
Fig. 9 is a block diagram of an electronic device according to a hot spot event determining method in an embodiment of the present application. 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 9, the electronic apparatus includes: one or more processors 801, memory 802, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 9 illustrates an example of a processor 801.
The memory 802 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the hot spot event determining method provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the hotspot event determination method provided by the present application.
The memory 802 serves as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the hot spot event determination method in the embodiment of the present application (for example, the grabbing unit 710, the event extraction unit 720, the feature extraction unit 730, the first determination unit 740, the second determination unit 750, the material mining unit 760, the first acquisition unit 770, and the second acquisition unit 780 shown in fig. 7). The processor 801 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 802, that is, implements the hotspot event determination method in the above method embodiments.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the electronic device of the hot event determination method, and the like. Further, the memory 802 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 802 optionally includes memory located remotely from the processor 801, which may be connected to the electronic device of the hotspot event determination method over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the hotspot event determination method may further include: an input device 803 and an output device 804. The processor 801, the memory 802, the input device 803, and the output device 804 may be connected by a bus or other means, and are exemplified by a bus in fig. 9.
The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device of the hot event determination method, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 804 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The Display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) Display, and a plasma Display. In some implementations, the display device can be a touch screen.
According to an embodiment of the present application, the present application also provides an electronic device. The apparatus may include:
one or more processors; and
and a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the search term recommendation method in the above method embodiments.
The functions and implementations of the processor and the storage device of the electronic device may refer to the descriptions about the processor and the memory in the above embodiments of the electronic device.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a Programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
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 Cathode Ray Tube (CRT) or LCD monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), and the internet.
The computer system may include clients and servers. A client and server are generally 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 may be a cloud server, which is also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in a conventional physical host and Virtual Private Server (VPS) service. The server may also be a server of a distributed system, or a server incorporating a blockchain.
According to the technical scheme of the embodiment of the application, event semantic understanding is carried out on the captured hot webpage resources to obtain a plurality of original events; performing semantic correlation matching on the search frequency characteristics and a plurality of original events to generate event dimension characteristics; screening out hot events from a plurality of original events according to the event dimension characteristics and the searching frequency characteristics of the target searching words; therefore, hot events of the whole network can be automatically identified, and compared with a mode of manually screening the hot events, the method and the device for screening the hot events have the advantages of wider screening range and higher screening speed; and hot events are screened by combining the search frequency characteristics and the event dimension characteristics, so that the efficiency and the accuracy of identifying the hot events in the whole network can be improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (15)
1. A hotspot event determination method, comprising:
capturing hot webpage resources;
performing event semantic understanding on the hot webpage resources to obtain a plurality of original events;
performing semantic correlation matching on the target search word and the plurality of original events to generate event dimension characteristics;
and screening out hot events from the plurality of original events according to the event dimension characteristics and the search frequency characteristics of the target search terms.
2. The method of claim 1, further comprising:
and determining a target hotspot event according to the indication information of whether the hotspot event is operated.
3. The method of claim 2, further comprising:
and carrying out material mining on the target hot event to obtain a material resource corresponding to the target hot event.
4. The method of claim 1, further comprising:
acquiring a target semantic model for event semantic understanding, wherein the target semantic model is obtained after a preset semantic model is trained on the basis of a first training sample;
performing event semantic understanding on the hot webpage resources to obtain a plurality of original events, including:
and inputting the hot webpage resources into the target semantic model to obtain a plurality of original events output by the target semantic model.
5. The method of claim 1, further comprising:
acquiring a target event model, wherein the target event model is obtained after a preset event model is trained on the basis of a second training sample and is used for screening hot events according to event dimension characteristics and search frequency characteristics;
screening out hot events from the multiple original events according to the event dimension characteristics and the search frequency characteristics of the target search terms, wherein the hot events comprise:
inputting the event dimension characteristics and the search frequency characteristics of the target search terms into the target event model to obtain the hot events which are output by the target event model and screened from the plurality of original events.
6. The method of claim 5, wherein training the preset event model based on the second training sample comprises:
and training a preset event model based on the sample event dimension characteristic and the sample search frequency characteristic corresponding to the second training sample.
7. A hotspot event determination device, comprising:
the capturing unit is used for capturing hot webpage resources;
the event extraction unit is used for performing event semantic understanding on the hot webpage resources to obtain a plurality of original events;
the feature extraction unit is used for performing semantic relevance matching on the target search terms and the plurality of original events to generate event dimension features;
and the first determining unit is used for screening out hot events from the plurality of original events according to the event dimension characteristics and the search frequency characteristics of the target search terms.
8. The apparatus of claim 7, further comprising:
and the second determining unit is used for determining the target hotspot event according to the indication information of whether the hotspot event is operated.
9. The apparatus of claim 8, further comprising:
and the material mining unit is used for mining the material aiming at the target hot spot event to obtain the material resource corresponding to the target hot spot event.
10. The apparatus of claim 7, further comprising:
the event semantic understanding system comprises a first obtaining unit, a second obtaining unit and a semantic model generating unit, wherein the first obtaining unit is used for obtaining a target semantic model for event semantic understanding, and the target semantic model is obtained after a preset semantic model is trained on the basis of a first training sample;
the event extraction unit is used for:
and inputting the hot webpage resources into the target semantic model to obtain a plurality of original events output by the target semantic model.
11. The apparatus of claim 7, further comprising:
the second obtaining unit is used for obtaining a target event model, wherein the target event model is obtained after a preset event model is trained on the basis of a second training sample and is used for screening hot events according to the event dimension characteristics and the searching frequency characteristics;
wherein the first determining unit is configured to:
inputting the event dimension characteristics and the search frequency characteristics of the target search terms into the target event model to obtain the hot events which are output by the target event model and screened from the plurality of original events.
12. The device of claim 11, wherein the target event model is obtained by training a preset event model based on the sample event dimensional feature and the sample search frequency feature corresponding to the second training sample.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
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 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
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