CN110728138A - News text recognition method and device and storage medium - Google Patents
News text recognition method and device and storage medium Download PDFInfo
- Publication number
- CN110728138A CN110728138A CN201910914065.XA CN201910914065A CN110728138A CN 110728138 A CN110728138 A CN 110728138A CN 201910914065 A CN201910914065 A CN 201910914065A CN 110728138 A CN110728138 A CN 110728138A
- Authority
- CN
- China
- Prior art keywords
- keyword
- determining
- news
- news text
- label
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses a news text recognition method, a news text recognition device and a storage medium. Wherein, the method comprises the following steps: determining a first keyword contained in a news text to be identified; determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and identifying the news text according to the label corresponding to the first keyword. Therefore, the technical problems that the accuracy is low and the coverage rate is not wide when the keywords are manually extracted to identify the news texts are solved.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method, an apparatus, and a storage medium for identifying a news text.
Background
News is a channel for people to obtain information, and people can know things around and on the society all the time through the news. In particular to the time news, the system can make people know the development policy of the society at present and important events. It is well known that the type and amount of news are particularly large, and a large amount of different news is generated every day, which is not easy for people to obtain hot news from a large amount of news. The existing technical scheme is to identify and classify news by adopting a mode of manually extracting keywords, but the accuracy rate of the mode is not high, and the coverage rate is not wide.
Aiming at the technical problems that the accuracy is low and the coverage rate is not wide when the keywords are manually extracted to identify the news texts in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device and a storage medium for identifying news texts, so as to at least solve the technical problems of low accuracy and low coverage rate of identifying news texts by manually extracting keywords in the prior art.
According to an aspect of an embodiment of the present disclosure, there is provided a method of news text recognition, including: determining a first keyword contained in a news text to be identified; determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and identifying the news text according to the label corresponding to the first keyword.
According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is executed.
According to another aspect of the embodiments of the present disclosure, there is also provided an apparatus for recognizing news text, including: the first determining module is used for determining a first keyword contained in the news text to be identified; the calculation module is used for determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and the identification module is used for identifying the news text according to the label corresponding to the first keyword.
According to another aspect of the embodiments of the present disclosure, there is also provided an apparatus for recognizing news text, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: determining a first keyword contained in a news text to be identified; determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and identifying the news text according to the label corresponding to the first keyword.
In the embodiment of the disclosure, a keyword (first keyword) contained in a news text is determined first, and then a tag corresponding to the first keyword is determined by using a preset correspondence rule, wherein the tag is determined from hot news and has a mapping relation with the keyword. And finally, identifying the news text according to the label, namely determining the hot spot to which the news text belongs. Therefore, the technical effect of effectively improving the work efficiency and accuracy of news hotspot identification is achieved. And further, the technical problems that the accuracy is low and the coverage rate is not wide when the news text is identified by manually extracting the keywords in the prior art are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;
fig. 2 is a schematic flow chart of a method for recognizing news text according to a first aspect of embodiment 1 of the present disclosure;
fig. 3 is a schematic diagram of a device for news text recognition according to embodiment 2 of the present disclosure; and
fig. 4 is a schematic diagram of a device for identifying news texts according to embodiment 3 of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to the present embodiment, there is provided an embodiment of a method of newsletter text recognition, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions and that, while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Fig. 1 illustrates a block diagram of a hardware architecture of a computing device for implementing a method for news text recognition. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path connected to the interface).
The memory may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for identifying news texts in the embodiments of the present disclosure, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory, that is, implementing the above-mentioned method for identifying news texts of application programs. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device 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 transmission device is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.
It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.
In the foregoing operating environment, according to a first aspect of the present embodiment, there is provided a method for identifying news text, where fig. 2 shows a flowchart of the method, and with reference to fig. 2, the method includes:
s202: determining a first keyword contained in a news text to be identified;
s204: determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and
s206: and identifying the news text according to the label corresponding to the first keyword.
As described in the background, news is a channel through which people can learn things around and in society at any time. In particular to the time news, the system can make people know the development policy of the society at present and important events. It is well known that the type and amount of news are particularly large, and a large amount of different news is generated every day, which is not easy for people to obtain hot news from a large amount of news. The existing technical scheme is to identify and classify news by adopting a mode of manually extracting keywords, but the accuracy rate of the mode is not high, and the coverage rate is not wide.
For the technical problems existing in the background art, the technical solution of the embodiment first determines a first keyword included in a news text to be identified. For example: the first keyword includes: national festival, holidays, travel, Beijing, tourists, and the like. Further, the label corresponding to the first keyword is determined by using a preset corresponding rule. Wherein the mapping relationship between the keyword and the tag is recorded by the corresponding rule, for example: the labels corresponding to the keywords of tourists and tourists are tourism, and the labels corresponding to the festival of national celebration and the festival of mid-autumn are festivals. The tags corresponding to the first keywords contained in the news text can be travel and holidays. Finally, the news text is identified (classified) according to the label, namely the hot spot information of the news text is travel at the festival of national celebration.
In this way, the keywords (first keywords) contained in the news text are firstly determined, and then the labels corresponding to the first keywords are determined according to the preset corresponding rules, wherein the labels are determined from the hot news and have mapping relation with the keywords. And finally, identifying the news text according to the label, namely determining the hot spot to which the news text belongs. Therefore, the technical effect of effectively improving the work efficiency and accuracy of news hotspot identification is achieved. And further, the technical problems that the accuracy is low and the coverage rate is not wide when the news text is identified by manually extracting the keywords in the prior art are solved.
Optionally, determining the corresponding rule according to the following steps: acquiring hot news information; determining a second keyword contained in the news hotspot information; and mapping the second keyword to a corresponding label, and determining a corresponding rule.
Specifically, in the process of determining the corresponding rule, hot news information, that is, hot news in different fields, is obtained first. Further, keywords are extracted from the news hotspot information. For example: the hotspot information is 'celebration for the long vacation in the eleventh country', and the included keywords (second keywords) include: eleven, festival of national day, tour. Finally, the second keyword is mapped to a corresponding tag, i.e. the keyword is tagged, for example: the label of the tour keyword is tour. Therefore, by the mode, the corresponding rule can be determined according to the hot news in different fields, and further preparation is made for news identification.
Optionally, the operation of determining a first keyword contained in the news text to be recognized includes: determining a plurality of words contained in the news text, and calculating the occurrence times of each word in the plurality of words in the news text; sorting the plurality of words according to the times; and determining a first keyword according to the sorting result.
Specifically, in the operation of determining the first keyword included in the news text to be recognized, a plurality of words included in the news text are first determined, and the number of occurrences of each word is calculated. For example: the above news text shows "national day festival" 10 times and "holiday" 8 times. Further, the keywords (first keywords) are sorted according to the times, and the sequence may be from more times to less times, or from less times to more times. Finally, a first keyword is determined according to the number of times, for example: the sequence of the sorting is from more to less, and then 5 words are selected as the first keyword according to the sequence. In this way, several keywords of the news text can thus be determined which are high in frequency.
Optionally, before the operation of determining the first keyword included in the news text to be recognized, the method further includes: news text is received from the monitoring APP.
In particular, news text is collected by monitoring the APP. The monitoring APP can be installed on the terminal device, collects news texts in real time and then transmits the news texts back. Thereby guaranteeing the real-time performance of news.
Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.
Thus, according to the present embodiment, a keyword (first keyword) included in a news text is first determined, and then a tag corresponding to the first keyword is determined according to a preset correspondence rule, where the tag is determined from hot news and has a mapping relationship with the keyword. And finally, identifying the news text according to the label, namely determining the hot spot to which the news text belongs. Therefore, the technical effect of effectively improving the work efficiency and accuracy of news hotspot identification is achieved. And further, the technical problems that the accuracy is low and the coverage rate is not wide when the news text is identified by manually extracting the keywords in the prior art are solved.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
Fig. 3 shows an apparatus 300 for news text recognition according to the present embodiment, the apparatus 300 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 3, the apparatus 300 includes: a first determining module 310, configured to determine a first keyword included in the news text to be recognized; the calculation module 320 is configured to determine, by using a preset correspondence rule, a tag corresponding to the first keyword, where the correspondence rule records a mapping relationship between the keyword and the tag; the identifying module 330 is configured to identify the news text according to the tag corresponding to the first keyword.
Optionally, the method further comprises: the acquisition module is used for acquiring hot news information; the second determining module is used for determining a second keyword contained in the news hotspot information; and the mapping module is used for mapping the second key words into corresponding labels and determining corresponding rules.
Optionally, the first determining module 310 includes: the statistic submodule is used for determining a plurality of words contained in the news text and calculating the occurrence frequency of each word in the plurality of words in the news text; the sequencing submodule is used for sequencing a plurality of words according to the times; and the determining submodule is used for determining the first keyword according to the sequencing result.
Optionally, the method further comprises: and the receiving module is used for receiving news texts from the monitoring APP.
Thus, according to the present embodiment, the apparatus 300 for identifying a news text first determines a keyword (first keyword) contained in the news text, and then determines a tag corresponding to the first keyword according to a preset rule, where the tag is determined from hot news and has a mapping relationship with the keyword. And finally, identifying the news text according to the label, namely determining the hot spot to which the news text belongs. Therefore, the technical effect of effectively improving the work efficiency and accuracy of news hotspot identification is achieved. And further, the technical problems that the accuracy is low and the coverage rate is not wide when the news text is identified by manually extracting the keywords in the prior art are solved.
Example 3
Fig. 4 shows an apparatus 400 for news text recognition according to the present embodiment, the apparatus 400 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 4, the apparatus 400 includes: a processor 410; and a memory 420 coupled to the processor 410 for providing instructions to the processor 410 to process the following process steps: determining a first keyword contained in a news text to be identified; determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and identifying the news text according to the label corresponding to the first keyword.
Optionally, the memory 420 is further configured to provide the processor 410 with instructions to process the following to determine the corresponding rule: acquiring hot news information; determining a second keyword contained in the news hotspot information; and mapping the second keyword to a corresponding label, and determining a corresponding rule.
Optionally, the operation of determining a first keyword contained in the news text to be recognized includes: determining a plurality of words contained in the news text, and calculating the occurrence times of each word in the plurality of words in the news text; sorting the plurality of words according to the times; and determining a first keyword according to the sorting result.
Optionally, the memory 420 is further configured to provide the processor 410 with instructions to process the following processing steps: the news text is received from the monitoring APP prior to an operation of determining a first keyword contained in the news text to be recognized.
Thus, according to the embodiment, the apparatus 400 for identifying news text first determines the keyword (first keyword) contained in the news text, and then determines the tag corresponding to the first keyword according to the preset corresponding rule, wherein the tag is determined from the hot news and has a mapping relation with the keyword. And finally, identifying the news text according to the label, namely determining the hot spot to which the news text belongs. Therefore, the technical effect of effectively improving the work efficiency and accuracy of news hotspot identification is achieved. And further, the technical problems that the accuracy is low and the coverage rate is not wide when the news text is identified by manually extracting the keywords in the prior art are solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of news text recognition, comprising:
determining a first keyword contained in a news text to be identified;
determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and
and identifying the news text according to the label corresponding to the first keyword.
2. The method of claim 1, further comprising determining the correspondence rule according to the steps of:
acquiring hot news information;
determining a second keyword contained in the news hotspot information; and
and mapping the second keyword to a corresponding label, and determining the corresponding rule.
3. The method of claim 1, wherein determining the first keyword contained in the news text to be identified comprises:
determining a plurality of words contained in news text and calculating the number of times each word in the plurality of words appears in the news text;
sorting the plurality of words according to the times; and
and determining the first keyword according to the sequencing result.
4. The method of claim 1, wherein the operation of determining the first keyword contained in the news text to be recognized further comprises: receiving the news text from a monitoring APP.
5. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 4 is performed by a processor when the program is run.
6. An apparatus for news text recognition, comprising:
the first determining module is used for determining a first keyword contained in the news text to be identified;
the calculation module is used for determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and
and the identification module is used for identifying the news text according to the label corresponding to the first keyword.
7. The apparatus of claim 6, further comprising:
the acquisition module is used for acquiring hot news information;
the second determining module is used for determining a second keyword contained in the news hotspot information; and
and the mapping module is used for mapping the second key words into corresponding labels and determining the corresponding rules.
8. The apparatus of claim 6, wherein the first determining module comprises:
the statistic submodule is used for determining a plurality of words contained in news text and calculating the number of times of occurrence of each word in the plurality of words in the news text;
the sequencing submodule is used for sequencing the words according to the times; and
and the determining submodule is used for determining the first keyword according to the sequencing result.
9. The apparatus of claim 6, further comprising: and the receiving module is used for receiving the news text from the monitoring APP.
10. An apparatus for news text recognition, comprising:
a processor; and
a memory coupled to the processor for providing instructions to the processor for processing the following processing steps:
determining a first keyword contained in a news text to be identified;
determining a label corresponding to the first keyword by using a preset corresponding rule, wherein the corresponding rule records a mapping relation between the keyword and the label; and
and identifying the news text according to the label corresponding to the first keyword.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910914065.XA CN110728138A (en) | 2019-09-25 | 2019-09-25 | News text recognition method and device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910914065.XA CN110728138A (en) | 2019-09-25 | 2019-09-25 | News text recognition method and device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110728138A true CN110728138A (en) | 2020-01-24 |
Family
ID=69219443
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910914065.XA Pending CN110728138A (en) | 2019-09-25 | 2019-09-25 | News text recognition method and device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110728138A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113496118A (en) * | 2020-04-07 | 2021-10-12 | 北京中科闻歌科技股份有限公司 | News subject identification method, equipment and computer readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107797982A (en) * | 2016-08-31 | 2018-03-13 | 百度在线网络技术（北京）有限公司 | For identifying the method, apparatus and equipment of text type |
| CN109992646A (en) * | 2019-03-29 | 2019-07-09 | 腾讯科技（深圳）有限公司 | The extracting method and device of text label |
-
2019
- 2019-09-25 CN CN201910914065.XA patent/CN110728138A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107797982A (en) * | 2016-08-31 | 2018-03-13 | 百度在线网络技术（北京）有限公司 | For identifying the method, apparatus and equipment of text type |
| US20190050396A1 (en) * | 2016-08-31 | 2019-02-14 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method, apparatus and device for recognizing text type |
| CN109992646A (en) * | 2019-03-29 | 2019-07-09 | 腾讯科技（深圳）有限公司 | The extracting method and device of text label |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113496118A (en) * | 2020-04-07 | 2021-10-12 | 北京中科闻歌科技股份有限公司 | News subject identification method, equipment and computer readable storage medium |
| CN113496118B (en) * | 2020-04-07 | 2024-05-31 | 北京中科闻歌科技股份有限公司 | News main body recognition method, device and computer readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110852231A (en) | Illegal video detection method and device and storage medium | |
| CN110782318A (en) | Marketing method and device based on audio interaction and storage medium | |
| CN110798703A (en) | Method and device for detecting illegal video content and storage medium | |
| CN110633458A (en) | Method and device for generating referee document | |
| CN106997431B (en) | Data processing method and device | |
| CN110837581A (en) | Method, device and storage medium for video public opinion analysis | |
| CN109190119B (en) | Time extraction method and device, storage medium and electronic device | |
| CN114398672A (en) | Authority-based flow creation method and device and storage medium | |
| CN110909110A (en) | Address standardization method and device, storage medium and processor | |
| CN110728138A (en) | News text recognition method and device and storage medium | |
| CN111340911A (en) | Method and device for determining connecting line in k-line graph and storage medium | |
| CN107169005B (en) | POI recommendation method, device, equipment and computer readable storage medium | |
| CN116737760A (en) | Data query method and device, nonvolatile storage medium and electronic equipment | |
| CN101304565B (en) | Method and unit for recognizing mobile terminal as well as method and system for providing increment service | |
| CN108319721B (en) | Application program recommendation method and device, user terminal and server | |
| CN110677824A (en) | Short message sending method, device and storage medium | |
| CN108268545B (en) | Method and device for establishing hierarchical user label library | |
| CN115861606A (en) | Method and device for classifying long-tail distribution documents and storage medium | |
| CN110888573A (en) | Method and device for identifying control and storage medium | |
| CN112601106B (en) | Video image processing method and device and storage medium | |
| CN115905459A (en) | Question answer prediction method, device and storage medium | |
| CN115249013A (en) | Method and device for identifying nested named entities and storage medium | |
| CN110647543A (en) | Data aggregation method, device and storage medium | |
| CN110807696A (en) | Method and device for matching funds with assets and storage medium | |
| CN111858994A (en) | Character retrieval method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |