CN112000768A - Device and method for determining correlation degree and computer equipment - Google Patents
Device and method for determining correlation degree and computer equipment Download PDFInfo
- Publication number
- CN112000768A CN112000768A CN202010760591.8A CN202010760591A CN112000768A CN 112000768 A CN112000768 A CN 112000768A CN 202010760591 A CN202010760591 A CN 202010760591A CN 112000768 A CN112000768 A CN 112000768A
- Authority
- CN
- China
- Prior art keywords
- user
- unread message
- keywords
- unread
- information
- 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
- 238000000034 method Methods 0.000 title claims description 35
- 238000012216 screening Methods 0.000 claims abstract description 32
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 230000011218 segmentation Effects 0.000 claims description 36
- 238000004590 computer program Methods 0.000 claims description 9
- 241000251468 Actinopterygii Species 0.000 description 8
- 235000012054 meals Nutrition 0.000 description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 229920000049 Carbon (fiber) Polymers 0.000 description 2
- 241000238557 Decapoda Species 0.000 description 2
- 239000004917 carbon fiber Substances 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 235000013547 stew Nutrition 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000035922 thirst Effects 0.000 description 2
- 235000017166 Bambusa arundinacea Nutrition 0.000 description 1
- 235000017491 Bambusa tulda Nutrition 0.000 description 1
- 241001330002 Bambuseae Species 0.000 description 1
- 235000015334 Phyllostachys viridis Nutrition 0.000 description 1
- 239000011425 bamboo Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
Abstract
The invention provides a device for determining correlation, comprising: the determining module is used for determining keywords related to the user information and keywords of the unread message; the calculation module is used for calculating the correlation degree of the keywords related to the user information and the keywords of the unread messages; and the screening module is used for screening out the unread messages to the users according to the correlation degree. The device provided by the invention can effectively screen out unread information related to the user for the user to read, thereby preventing valuable information from being missed.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to an apparatus, a method, and a computer device for determining a degree of correlation.
Background
At present, the application of the Internet is rapidly developed, and social networking software is like bamboo shoots which grow endlessly in spring after rain. In a group chat scene of chat software, when unread messages are too many, a user reads the messages one by one, which wastes time and labor; if all unread messages are ignored, the omission of key information may be caused.
Disclosure of Invention
The device, the method and the computer equipment for determining the relevancy can solve the problem that unread messages cannot be effectively screened out when the unread messages of a user are too much during chatting.
In order to achieve the above object, a first aspect of the present invention provides an apparatus for determining a degree of correlation, comprising: the determining module is used for determining keywords related to the user information and keywords of the unread message; the calculation module is used for calculating the correlation degree of the keywords related to the user information and the keywords of the unread messages; and the screening module is used for screening out the unread messages to the users according to the correlation degree.
A second aspect of the present invention provides a method of determining a degree of correlation, comprising: determining keywords related to user information and keywords of unread messages; calculating the relevancy of the keywords related to the user information and the keywords of the unread messages; and screening out unread messages to the user according to the correlation.
A third aspect of the invention provides a computer apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to cause the computer apparatus to perform the method of the second aspect.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a computer, implements the method of the second aspect.
The invention has the beneficial effects that:
the invention provides a device and a method for determining relevance, computer equipment and a computer readable storage medium, which can effectively screen out unread information relevant to a user for the user to read and prevent valuable information from being missed.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention.
Fig. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention;
fig. 4 is a schematic connection diagram of a storage medium according to a fourth embodiment of the present invention.
Detailed Description
Various embodiments of the present invention will be described more fully hereinafter. The invention is capable of various embodiments and of modifications and variations therein. However, it should be understood that: there is no intention to limit various embodiments of the invention to the specific embodiments disclosed herein, but on the contrary, the intention is to cover all modifications, equivalents, and/or alternatives falling within the spirit and scope of various embodiments of the invention.
Hereinafter, the terms "includes" or "may include" used in various embodiments of the present invention indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of the present invention, the terms "comprises," "comprising," "includes," "including," "has," "having" and their derivatives are intended to mean that the specified features, numbers, steps, operations, elements, components, or combinations of the foregoing, are only meant to indicate that a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as first excluding the existence of, or adding to the possibility of, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
In various embodiments of the invention, the expression "a or/and B" includes any or all combinations of the words listed simultaneously, e.g., may include a, may include B, or may include both a and B.
Expressions (such as "first", "second", and the like) used in various embodiments of the present invention may modify various constituent elements in various embodiments, but may not limit the respective constituent elements. For example, the above description does not limit the order and/or importance of the elements described. The foregoing description is for the purpose of distinguishing one element from another. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of various embodiments of the present invention.
It should be noted that: in the present invention, unless otherwise explicitly stated or defined, the terms "mounted," "connected," "fixed," and the like are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; there may be communication between the interiors of the two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, it should be understood by those skilled in the art that the terms indicating an orientation or a positional relationship herein are based on the orientations and the positional relationships shown in the drawings and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the device or the element referred to must have a specific orientation, be constructed in a specific orientation and operate, and thus, should not be construed as limiting the present invention.
The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the invention. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
Referring to fig. 1, fig. 1 shows an apparatus 100 for determining correlation according to an embodiment of the present invention, the apparatus includes:
a determining module 110, configured to determine keywords related to user information and keywords of unread messages;
a calculating module 120, configured to calculate relevancy of the keyword related to the user information and the keyword of the unread message;
and the screening module 130 is configured to screen out the unread message to the user according to the relevance. Specifically, the screening module is specifically configured to screen out the unread messages with the relevancy greater than or equal to a first threshold to the user. The first threshold value can be set according to the actual situation.
Optionally, the determining module is specifically configured to determine at least one of a user name, a user nickname, and a user mobile phone number as the first keyword related to the user information. The calculation module is specifically configured to determine that the correlation degree is 1 when the unread message directly calls at least one of the user name, the user nickname, and the user mobile phone number. Specifically, the first keyword is information directly related to or uniquely corresponding to the information of the user, and includes at least one of a user name, a user nickname, and a user mobile phone number. And when the calculation module finds that at least one of the user name, the user nickname and the user mobile phone number is directly called in the unread message, the calculation of the correlation degree is finished, the correlation degree is determined to be 1, if the information of the @ user nickname, the @ user name or the @ user mobile phone number is found in the unread message, the correlation degree of the information is determined to be 1, the information is directly screened out to the user, and the user is prompted. Illustratively, the user nickname is "smile", when the unread message is "when to go home to eat? And @ smile ", determining that the relevancy of the unread message is 1, screening the unread message, and prompting a user to read.
Optionally, the determining module is specifically configured to screen nouns and verbs in the unread messages one by one as keywords of the unread messages, and specifically, when the determining module screens the keywords of the unread messages, a method of word segmentation may be adopted, for example, the verbs and the names are extracted from the messages one by one, so as to obtain the keywords of the unread messages. For example, when the unread message is "when to go home to eat? ", then it may be determined that the keywords include" go back "," home "," eat "," meal ". The determining module is specifically configured to determine at least one of the following information as the second keyword related to the user information: determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; a user name; a user nickname; a user mobile phone number; and custom keywords. The calculation module is specifically configured to determine that the relevancy is 1 when the second keyword is matched in the unread message. Specifically, the second keyword may include at least one of the following 5 pieces of information: 1. determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; 2. a user name; 3. a user nickname; 4. a user mobile phone number; and 5, self-defined keywords. And the calculation module matches the second keyword with the keywords of the unread message, and when the second keyword is determined to appear in the unread message, the matching is determined to be successful, the relevancy is determined to be 1, and the information is directly screened out to the user. If some unread message keyword includes "eat" and the second keyword also includes the keyword "eat", the relevancy of the unread message is determined to be 1, and the unread message is screened out to prompt the user to read.
When none of the keywords of the unread message matches the first keyword and the second keyword, the relevancy is determined according to the following manner.
Optionally, the determining module is specifically configured to filter out nouns and verbs in the unread messages item by item as keywords of the unread messages. Specifically, when the determining module filters the keywords of the unread message, a method of word segmentation processing may be adopted, for example, verbs and names are extracted from each message, so as to obtain the keywords of the unread message. For example, when the unread message is "when to go home to eat? ", then it may be determined that the keywords include" go back "," home "," eat "," meal ". The determining module is specifically configured to determine the following information as the third keyword related to the user information: determining three pieces of information sent by a user before the unread message, screening out verbs and nouns in the three pieces of information, and generating a vocabulary with the similar meaning to the verbs and nouns as the third key words. For example, the three pieces of information sent by the user before the unread message may include "return to a room to eat", "i hungry", "water to boil fish", and then the keywords of the three pieces of information include "return to a room", "use", "eat", "i hungry", "water", "boil", and "fish". Further, the generating of the words with similar meanings includes finding the words with similar meanings from a preset word group, for example, the third keyword may include: "Hui" "Jia" "eat" "meal" "Wu" "thirst" "river" "stew" "shrimp".
The calculation module is specifically configured to determine the relevancy D of the keyword related to the user information and the keyword of the unread message according to the following formula:
D＝D1*r1+D2*r2；
said D1Including a degree of keyword matching of the third keyword with unread messages, the D2Including the probability that three pieces of information sent by a user before the unread message appear in the unread message, r1Is a first weight value, r2Is the second weight. Optionally, the r is180%, said r2The weight can be set according to the actual situation as 20%.
Optionally, the
And the X is the number of the third keywords successfully matched with the keywords of the unread message, and the X is the total number of the keywords of the unread message. For example, the unread message is "when to go home to eat? ", three pieces of information sent by the user before the unread message may include" return to home for dinner "," i hungry "," water boiled fish ", X is 4, D1＝100％。
Optionally, the
Z is1The three pieces of information sent by the user before the unread message are obtained by adopting unary word segmentationThe number of the obtained words matched with the words obtained by adopting the unary word segmentation to the unread message, and Z1The number of words obtained by using unary word segmentation for the unread message, z2The number of words obtained by binary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by binary word segmentation of the unread message, and the Z2The number of words obtained by binary word segmentation on the unread message, z3The number of words obtained by ternary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by ternary word segmentation of the unread message, and the number of the words is Z3The number of words obtained by ternary word segmentation for the unread message, r3Is a third weight value, r4Is a fourth weight, said r5Is the fifth weight. Optionally, the r is320%, said r450%, said r5The amount of the carbon fiber can be set to 30% according to the actual situation. Illustratively, again, the unread message is taken as "when to go home to eat? If the three pieces of information sent by the user before the unread message include "return to home for dinner", "I hungry" and "water-boiled fish", the sentence of the unread message is divided into one Chinese character when the unread message is divided into words in one unit, and the unread message includes 8 Chinese characters, so that Z is the number of Chinese characters, and the method is suitable for the users to read the unread message in the prior art1The three pieces of information include 10 chinese characters, so z is 810. Similarly, when binary word segmentation is performed, every two words in a sentence from beginning to end form a word, and Z is2＝4，z20. When carrying out ternary word segmentation, forming each three words of a sentence into a word from beginning to end, Z3＝2，z30. Further, D can be calculated20, D-1-0.8 + 0.2-0.8. And screening the unread message to the user when the first threshold is set to be 50%.
The device for determining the relevance can effectively screen out unread information relevant to the user for the user to read, and prevents valuable information from being omitted.
Referring to fig. 2, fig. 2 shows a method for determining a correlation according to a second embodiment of the present invention, including:
in step 210, keywords associated with user information are determined, as well as keywords for unread messages.
And step 230, screening out the unread messages to the user according to the relevance. Specifically, the screening out the unread messages to the user according to the relevance includes: screening out the unread messages with the correlation degree larger than or equal to a first threshold value to the user. The first threshold value can be set according to the actual situation.
The determining the keywords related to the user information comprises the following steps: and determining at least one of a user name, a user nickname and a user mobile phone number as the first keyword related to the user information. The calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises: and when at least one of the user name, the user nickname and the user mobile phone number is directly called in the unread message, determining that the correlation degree is 1. Specifically, the first keyword is information directly related to or uniquely corresponding to the information of the user, and includes at least one of a user name, a user nickname, and a user mobile phone number. If at least one of the user name, the user nickname and the user mobile phone number is directly called in the unread message, the correlation calculation is finished, the correlation is determined to be 1, if the information of the @ user nickname, the @ user name or the @ user mobile phone number is found in the unread message, the correlation of the information is determined to be 1, the information is directly screened out to the user, and the user is prompted. Illustratively, the user nickname is "smile", when the unread message is "when to go home to eat? And @ smile ", determining that the relevancy of the unread message is 1, screening the unread message, and prompting a user to read.
Optionally, the determining the keyword of the unread message includes: and filtering nouns and verbs in the unread messages one by one to serve as keywords of the unread messages. Specifically, when the keywords of the unread message are screened, a word segmentation method may be adopted, for example, verbs and names are extracted from each message to obtain the keywords of the unread message. For example, when the unread message is "when to go home to eat? ", then it may be determined that the keywords include" go back "," home "," eat "," meal ".
Optionally, the determining the keyword related to the user information includes: determining at least one of the following information as the second keyword related to the user information: determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; a user name; a user nickname; a user mobile phone number; and, custom keywords. The calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises: and when the second keyword is matched in the unread message, determining that the correlation degree is 1. Specifically, the second keyword may include at least one of the following 5 pieces of information: 1. determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; 2. a user name; 3. a user nickname; 4. a user mobile phone number; and 5, self-defined keywords. And the calculation module matches the second keyword with the keywords of the unread message, and when the second keyword is determined to appear in the unread message, the matching is determined to be successful, the relevancy is determined to be 1, and the information is directly screened out to the user. If some unread message keyword includes "eat" and the second keyword also includes the keyword "eat", the relevancy of the unread message is determined to be 1, and the unread message is screened out to prompt the user to read.
Optionally, when the first keyword and the second keyword are not matched in the unread message, the determining the keyword of the unread message includes: and filtering nouns and verbs in the unread messages one by one to serve as keywords of the unread messages. Specifically, when the determining module filters the keywords of the unread message, a method of word segmentation processing may be adopted, for example, verbs and names are extracted from each message, so as to obtain the keywords of the unread message. For example, when the unread message is "when to go home to eat? ", then it may be determined that the keywords include" go back "," home "," eat "," meal ". The determining the keywords related to the user information comprises the following steps: determining the following information as the third keyword related to the user information: determining three pieces of information sent by a user before the unread message, screening out verbs and nouns in the three pieces of information, and generating a vocabulary with the similar meaning to the verbs and nouns as the third key words. For example, the three pieces of information sent by the user before the unread message may include "return to a room to eat", "i hungry", "water to boil fish", and then the keywords of the three pieces of information include "return to a room", "use", "eat", "i hungry", "water", "boil", and "fish". Further, the generating of the words with similar meanings includes finding the words with similar meanings from a preset word group, for example, the third keyword may include: "Hui" "Jia" "eat" "meal" "Wu" "thirst" "river" "stew" "shrimp".
The calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises: determining the relevancy D of the keywords related to the user information and the keywords of the unread messages according to the following formula:
D＝D1*r1+D2*r2；
said D1Including a degree of keyword matching of the third keyword with unread messages, the D2Including the probability that three pieces of information sent by a user before the unread message appear in the unread message, r1Is a first weight value, r2Is the second weight. Optionally, the r is180%, said r2The weight can be set according to the actual situation as 20%.
Optionally, the
The X is the number of the third keywords successfully matched with the keywords of the unread message, and the X is the total keywords of the unread messageThe number of (2). For example, the unread message is "when to go home to eat? ", three pieces of information sent by the user before the unread message may include" return to home for dinner "," i hungry "," water boiled fish ", X is 4, D1＝100％。
Optionally, the
Z is1The number of words obtained by using unary participle for three pieces of information sent by a user before the unread message is matched with the number of words obtained by using unary participle for the unread message, and the Z1The number of words obtained by using unary word segmentation for the unread message, z2The number of words obtained by binary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by binary word segmentation of the unread message, and the Z2The number of words obtained by binary word segmentation on the unread message, z3The number of words obtained by ternary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by ternary word segmentation of the unread message, and the number of the words is Z3The number of words obtained by ternary word segmentation for the unread message, r3Is a third weight value, r4Is a fourth weight, said r5Is the fifth weight. Optionally, the r is320%, said r450%, said r5The amount of the carbon fiber can be set to 30% according to the actual situation. Illustratively, again, the unread message is taken as "when to go home to eat? If the three pieces of information sent by the user before the unread message include "return to home for dinner", "I hungry" and "water-boiled fish", the sentence of the unread message is divided into one Chinese character when the unread message is divided into words in one unit, and the unread message includes 8 Chinese characters, so that Z is the number of Chinese characters, and the method is suitable for the users to read the unread message in the prior art1The three pieces of information include 10 chinese characters, so z is 810. Similarly, when binary word segmentation is performed, every two words in a sentence from beginning to end form a word, and Z is2＝4，z20. When carrying out ternary word segmentation, every three character groups of the sentence are divided from beginning to endIn a word, Z3＝2，z30. Further, D can be calculated20, D-1-0.8 + 0.2-0.8. And screening the unread message to the user when the first threshold is set to be 50%.
The method for determining the relevancy can effectively screen out unread information relevant to the user for the user to read, and prevents valuable information from being omitted.
The third embodiment of the present invention further provides a computer device 300, as shown in fig. 3, the computer device includes: a memory 310, a processor 320 and a computer program stored on the memory and executable on the processor, the processor executing the computer program to cause the computer device 300 to perform the method of embodiment two. For other functions of the computer device 300, reference may be made to the description of the second embodiment, and further description is omitted here.
A storage medium 410 is further provided in the fourth embodiment of the present invention, as shown in fig. 4, where the program in the second embodiment is stored on the storage medium, and when being executed by the processor 420, the program implements the steps of the method in the second embodiment. The method can refer to the second embodiment, and details are not repeated here.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The above examples are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, other various changes and modifications can be made according to the above-described technical solutions and concepts, and all such changes and modifications should fall within the protection scope of the present invention.
Claims (20)
1. An apparatus for determining a degree of correlation, comprising:
the determining module is used for determining keywords related to the user information and keywords of the unread message;
the calculation module is used for calculating the correlation degree of the keywords related to the user information and the keywords of the unread messages;
and the screening module is used for screening out the unread messages to the users according to the correlation degree.
2. The apparatus of claim 1,
the determining module is specifically configured to determine at least one of a user name, a user nickname, and a user mobile phone number as the first keyword related to the user information;
the calculation module is specifically configured to determine that the correlation degree is 1 when the unread message directly calls at least one of the user name, the user nickname, and the user mobile phone number.
3. The apparatus of claim 2,
the determining module is specifically configured to screen nouns and verbs in the unread messages one by one as keywords of the unread messages;
the determining module is specifically configured to determine at least one of the following information as the second keyword related to the user information: determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; a user name; a user nickname; a user mobile phone number; and, a custom keyword;
the calculation module is specifically configured to determine that the relevancy is 1 when the second keyword is matched in the unread message.
4. The apparatus of claim 3,
the determining module is specifically configured to, when the first keyword and the second keyword are not matched in the unread message, screen out nouns and verbs in the unread message one by one as keywords of the unread message;
the determining module is specifically configured to determine the following information as the third keyword related to the user information: determining three pieces of information sent by a user before the unread message, screening out verbs and nouns in the three pieces of information, and generating a vocabulary with similar meanings to the verbs and the nouns as the third key words;
the calculation module is specifically configured to determine the relevancy D of the keyword related to the user information and the keyword of the unread message according to the following formula:
D＝D1*r1+D2*r2；
said D1Including the third key wordDegree of keyword match with unread messages, said D2Including the probability that three pieces of information sent by a user before the unread message appear in the unread message, r1Is a first weight value, r2Is the second weight.
5. The apparatus of claim 4,
said r180%, said r2＝20％。
6. The apparatus of claim 4,
the above-mentioned
And the X is the number of the third keywords successfully matched with the keywords of the unread message, and the X is the total number of the keywords of the unread message.
7. The apparatus of claim 4,
the above-mentioned
Z is1The number of words obtained by using unary participle for three pieces of information sent by a user before the unread message is matched with the number of words obtained by using unary participle for the unread message, and the Z1The number of words obtained by using unary word segmentation for the unread message, z2The number of words obtained by binary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by binary word segmentation of the unread message, and the Z2The number of words obtained by binary word segmentation on the unread message, z3The number of words obtained by ternary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by ternary word segmentation of the unread message, and the number of the words is Z3Obtaining the unread message by adopting ternary word segmentationNumber of words to, said r3Is a third weight value, r4Is a fourth weight, said r5Is the fifth weight.
8. The apparatus of claim 4,
said r320%, said r450%, said r5＝30％。
9. The apparatus of claim 1,
the screening module is specifically configured to screen the unread messages with the relevancy greater than or equal to a first threshold to the user.
10. A method of determining a degree of correlation, comprising:
determining keywords related to user information and keywords of unread messages;
calculating the relevancy of the keywords related to the user information and the keywords of the unread messages;
and screening out unread messages to the user according to the correlation.
11. The method of claim 10,
the determining the keywords related to the user information comprises the following steps:
determining at least one of a user name, a user nickname and a user mobile phone number as the first keyword related to the user information;
the calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises:
and when at least one of the user name, the user nickname and the user mobile phone number is directly called in the unread message, determining that the correlation degree is 1.
12. The method of claim 11,
the determining the keywords of the unread message includes:
screening nouns and verbs in the unread messages one by one to be used as keywords of the unread messages;
the determining the keywords related to the user information comprises the following steps:
determining at least one of the following information as the second keyword related to the user information: determining three pieces of information sent by a user before the unread message, and screening verbs and nouns in the three pieces of information as the keywords; a user name; a user nickname; a user mobile phone number; and, a custom keyword;
the calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises:
and when the second keyword is matched in the unread message, determining that the correlation degree is 1.
13. The method of claim 12, wherein when the first key and the second key are not matched in the unread message,
the determining the keywords of the unread message includes:
screening nouns and verbs in the unread messages one by one to be used as keywords of the unread messages;
the determining the keywords related to the user information comprises the following steps:
determining the following information as the third keyword related to the user information: determining three pieces of information sent by a user before the unread message, screening out verbs and nouns in the three pieces of information, and generating a vocabulary with similar meanings to the verbs and the nouns as the third key words;
the calculating the relevancy of the keywords related to the user information and the keywords of the unread messages comprises:
determining the relevancy D of the keywords related to the user information and the keywords of the unread messages according to the following formula:
D＝D1*r1+D2*r2；
said D1Including a degree of keyword matching of the third keyword with unread messages, the D2Including the probability that three pieces of information sent by a user before the unread message appear in the unread message, r1Is a first weight value, r2Is the second weight.
14. The method of claim 13,
said r180%, said r2＝20％。
15. The method of claim 13,
the above-mentioned
And the X is the number of the third keywords successfully matched with the keywords of the unread message, and the X is the total number of the keywords of the unread message.
16. The method of claim 13,
the above-mentioned
Z is1The number of words obtained by using unary participle for three pieces of information sent by a user before the unread message is matched with the number of words obtained by using unary participle for the unread message, and the Z1The number of words obtained by using unary word segmentation for the unread message, z2The number of words obtained by binary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by binary word segmentation of the unread message, and the Z2The number of words obtained by binary word segmentation on the unread message, z3The number of words obtained by ternary word segmentation of three pieces of information sent by a user before the unread message is matched with the number of words obtained by ternary word segmentation of the unread message,z is3The number of words obtained by ternary word segmentation for the unread message, r3Is a third weight value, r4Is a fourth weight, said r5Is the fifth weight.
17. The method of claim 16,
said r320%, said r450%, said r5＝30％。
18. The method of claim 10, wherein screening unread messages to users according to the relevancy comprises:
screening out the unread messages with the correlation degree larger than or equal to a first threshold value to the user.
19. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to cause the computer device to perform the method of any one of claims 10-18.
20. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a computer, carries out the method according to any one of claims 10-18.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010760591.8A CN112000768A (en) | 2020-07-31 | 2020-07-31 | Device and method for determining correlation degree and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010760591.8A CN112000768A (en) | 2020-07-31 | 2020-07-31 | Device and method for determining correlation degree and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112000768A true CN112000768A (en) | 2020-11-27 |
Family
ID=73464165
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010760591.8A Pending CN112000768A (en) | 2020-07-31 | 2020-07-31 | Device and method for determining correlation degree and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112000768A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023109831A1 (en) * | 2021-12-16 | 2023-06-22 | 维沃移动通信有限公司 | Message processing method and apparatus and electronic device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450505A (en) * | 2015-11-03 | 2016-03-30 | 北京金山安全软件有限公司 | Notification message processing method and device and terminal equipment |
| CN106231074A (en) * | 2016-07-15 | 2016-12-14 | 宇龙计算机通信科技(深圳)有限公司 | A kind of method and device of Message Processing |
| CN107018237A (en) * | 2017-04-28 | 2017-08-04 | 努比亚技术有限公司 | A kind of unread message processing method, terminal and computer-readable recording medium |
| CN109740152A (en) * | 2018-12-25 | 2019-05-10 | 腾讯科技（深圳）有限公司 | Determination method, apparatus, storage medium and the computer equipment of text classification |
-
2020
- 2020-07-31 CN CN202010760591.8A patent/CN112000768A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450505A (en) * | 2015-11-03 | 2016-03-30 | 北京金山安全软件有限公司 | Notification message processing method and device and terminal equipment |
| CN106231074A (en) * | 2016-07-15 | 2016-12-14 | 宇龙计算机通信科技(深圳)有限公司 | A kind of method and device of Message Processing |
| CN107018237A (en) * | 2017-04-28 | 2017-08-04 | 努比亚技术有限公司 | A kind of unread message processing method, terminal and computer-readable recording medium |
| CN109740152A (en) * | 2018-12-25 | 2019-05-10 | 腾讯科技（深圳）有限公司 | Determination method, apparatus, storage medium and the computer equipment of text classification |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023109831A1 (en) * | 2021-12-16 | 2023-06-22 | 维沃移动通信有限公司 | Message processing method and apparatus and electronic device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108509425B (en) | Chinese new word discovery method based on novelty | |
| CN101166159B (en) | A method and system for identifying rubbish information | |
| US11050685B2 (en) | Method for determining candidate input, input prompting method and electronic device | |
| CN108897734A (en) | User's portrait generation method, device, computer equipment and storage medium | |
| KR20110115543A (en) | Method for calculating entity similarities | |
| Morris et al. | Identifying Sexual Predators by SVM Classification with Lexical and Behavioral Features. | |
| RU2631164C2 (en) | Method of animating sms-messages | |
| CN108334494B (en) | Method and device for constructing user relationship network | |
| CN113204618A (en) | Information identification method, device and equipment based on semantic enhancement and storage medium | |
| CN101360074B (en) | Method and system determining suspicious spam range | |
| CN109450776A (en) | Cohesion calculation method, device, equipment and medium | |
| CN112000768A (en) | Device and method for determining correlation degree and computer equipment | |
| WO2016162844A1 (en) | Processing a search query and retrieving records from a local and server side database system of an electronic messaging system | |
| CN110011898B (en) | Reply method and device of e-mail, storage medium and computer equipment | |
| CN113128205B (en) | Scenario information processing method and device, electronic equipment and storage medium | |
| CN112492606A (en) | Classification and identification method and device for spam messages, computer equipment and storage medium | |
| CN105022821B (en) | Content filtering method and terminal | |
| CN116738956A (en) | Prompt template generation method and device, computer equipment and storage medium | |
| RU2583713C2 (en) | System and method of eliminating shingles from insignificant parts of messages when filtering spam | |
| CN111737425B (en) | Response method, device, server and storage medium | |
| RU2772404C2 (en) | System and method for determination of event classification rule on user terminal device | |
| CN111444716A (en) | Title word segmentation method, terminal and computer readable storage medium | |
| CN113869041A (en) | Keyword combination extraction method and device and electronic equipment | |
| CN111934930B (en) | Mail contact person recommending method and device, electronic equipment and storage medium | |
| CN110717011B (en) | Session message processing 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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230928 Address after: No. 15, Jiannan Road, Nanyou Community, Yuehai Street, Nanshan District, Shenzhen, Guangdong 518000 Hengda Tianjing B2801 Applicant after: Xingluo home yunwulian Technology Co.,Ltd. Address before: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Applicant before: HENGDA WISDOM TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right |