CN108241629A - Keyword group technology and device - Google Patents
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Abstract
The application proposes that a kind of keyword group technology and device, this method include：Determine the information of keyword to be grouped, described information includes：Effect is launched in part of speech and/or prediction；Grouping in existing grouping with the Keywords matching to be grouped is determined according to described information；The keyword to be grouped is divided into the matched grouping.This method can be automatically performed keyword grouping, so as to improve efficiency and accuracy and reduce cost.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a keyword grouping method and apparatus.
Background
In Search Engine Marketing (SEM) systems, advertisers are accustomed to placing keywords into different groups of their accounts, giving different bids, matching controls, creative copy, etc. to the keywords of different groups as desired.
In the related art, keywords are typically manually classified into different groups by advertisers. However, as the number of keywords increases and the labor cost increases, the way of dividing the keywords manually has problems in terms of efficiency, accuracy, cost, and the like.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
To this end, an object of the present application is to provide a keyword grouping method that can automatically complete keyword grouping, thereby improving efficiency and accuracy, and reducing cost.
Another object of the present application is to provide a keyword grouping apparatus.
In order to achieve the above object, an embodiment of the first aspect of the present application provides a keyword grouping method, including: determining information of keywords to be grouped, wherein the information comprises: part of speech and/or a predicted delivery effect; determining a group matched with the keywords to be grouped in the existing groups according to the information; and dividing the keywords to be grouped into the matched groups.
According to the keyword grouping method provided by the embodiment of the first aspect of the application, the information of the keywords to be grouped is determined, the groups matched with the keywords to be grouped are determined according to the information, and the keywords to be grouped are divided into the matched groups, so that the keywords can be automatically grouped without manual grouping, the efficiency and the accuracy are improved, and the cost is reduced.
In order to achieve the above object, an embodiment of a second aspect of the present application provides a keyword grouping apparatus, including: the first determining module is used for determining information of the keywords to be grouped, and the information comprises: part of speech and/or a predicted delivery effect; the second determining module is used for determining the groups matched with the keywords to be grouped in the existing groups according to the information; and the grouping module is used for dividing the keywords to be grouped into the matched groups.
According to the keyword grouping device provided by the embodiment of the second aspect of the application, the information of the keywords to be grouped is determined, the groups matched with the keywords to be grouped are determined according to the information, the keywords to be grouped are divided into the matched groups, the keywords can be automatically grouped, manual grouping is not needed, the efficiency and the accuracy are improved, and the cost is reduced.
An embodiment of the present application further provides an apparatus, including: one or more processors; a memory for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of the embodiments of the first aspect of the present application.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, where one or more programs stored in the storage medium, when executed by one or more processors of a device, cause the one or more processors to perform the method according to any one of the embodiments of the first aspect of the present application.
The embodiments of the present application also propose a computer program product, which when executed by one or more processors in a device causes the one or more processors to perform the method of any one of the embodiments of the first aspect of the present application.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart illustrating a keyword grouping method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a keyword grouping method according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a keyword grouping apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a keyword grouping apparatus according to another embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Fig. 1 is a flowchart illustrating a keyword grouping method according to an embodiment of the present application.
As shown in fig. 1, the method of the present embodiment includes:
s11: determining information of keywords to be grouped, wherein the information comprises: part of speech and/or predicted delivery effect.
Under different application scenarios, the keywords to be grouped may be different. For example, in the SEM system, the keywords to be grouped refer to keywords that need to be classified into groups of advertiser accounts. The obtaining mode of the keywords to be grouped is not limited, and for example, the keywords may be obtained by manual collection, or may be obtained by automatic word expansion technology.
Part of speech refers to a generalized part of speech and may include at least one of: business part of speech, entity part of speech, linguistic part of speech.
The service parts of speech are used to distinguish different services, for example, the service parts of speech include: factory words, alliance words, etc.
Entity parts of speech are used to distinguish different entities, for example, entity parts of speech include: regional words, brand words, etc.
Linguistic parts of speech refers to linguistic definitions, such as, for example, linguistic parts of speech including: nouns, verbs, adjectives, etc.
Specific methods for identifying the part of speech of the keyword can be seen in the following description.
The predicted putting effect is obtained after the putting effect of the keywords to be grouped is predicted, the specific putting effect can be set according to application requirements, for example, the putting effect comprises: and the showing rate, the click rate, the conversion rate and the like of the advertisements corresponding to the keywords to be grouped.
For a specific method for predicting the keyword release effect, reference may be made to the following description.
S12: and determining the grouping matched with the keywords to be grouped in the existing grouping according to the information.
The existing grouping is for example a grouping of advertiser accounts.
The existing grouping of advertiser accounts may be set according to application requirements, for example, the existing grouping of advertiser accounts includes: price word groupings, query word groupings, and translation word groupings.
Correspondingly, when matching, it needs to determine which of the three groups the keyword to be grouped belongs to. Specific matching methods can be found in the following description.
S13: and dividing the keywords to be grouped into the matched groups.
For example, if the grouping matched with the keywords to be grouped is the price word grouping, the keywords to be grouped are divided into the price word grouping.
It is to be understood that the to-be-grouped keyword may be discarded if it does not match any of the existing groups.
In the embodiment, the information of the keywords to be grouped is determined, the groups matched with the keywords to be grouped are determined according to the information, and the keywords to be grouped are divided into the matched groups, so that the keywords can be automatically grouped without manual grouping, the efficiency and the accuracy are improved, and the cost is reduced.
Fig. 2 is a flowchart illustrating a keyword grouping method according to another embodiment of the present application.
The present embodiment takes as an example the division of keywords into different groupings of advertiser accounts.
As shown in fig. 2, the method of the present embodiment includes:
s21: and determining the part of speech and the predicted delivery effect of the keywords to be grouped.
Part of speech refers to a generalized part of speech, including various part of speech categories, such as part of speech can be classified into the following part of speech categories: business part of speech, entity part of speech, and linguistic part of speech.
When identifying the parts of speech of a keyword, generally, one keyword can belong to only one of the same parts of speech, and one keyword can belong to a plurality of different parts of speech. For example, under the business part of speech, a keyword can be recognized only as a factory word or a member word, and a keyword can be recognized as both a factory word and a noun.
The parts of speech of different categories have different recognition modes, which are specifically as follows:
(1) identification of parts-of-speech of a service
The identification of the service part of speech is to identify whether the keyword to be grouped is a manufacturer word or a member word.
During specific identification, the method can be carried out based on substring matching, and the service part of speech to which the substring contained in the keyword to be grouped belongs is determined as the service part of speech of the keyword to be grouped. The substring included in each service part of speech may be predetermined by data mining or the like, for example, it may be determined that the franchising word generally includes the substring of "franchising", and therefore, if the keyword to be grouped includes the substring of "franchising", the keyword to be grouped may be identified as the franchising word.
For example, the service parts of speech are identified based on sub-string matching, and it is understood that the method is not limited to this method, and for example, a classification model may be used for classification. For example, different service parts of speech are regarded as different categories, and then the part of speech recognition task is converted into a classification task, and the service parts of speech of the keywords can be recognized through training and estimation of a classification model. The classification model can be a classification model in various related technologies, such as an SVM model.
(2) Identification of entity part-of-speech
The identification of the part of speech of the entity is to identify that the keyword is a regional word or a brand word.
The specific identification can be performed based on a sequence tagging model, and the sequence tagging model is widely applied to the text processing related fields, such as word segmentation, part of speech tagging, named entity identification and the like. The existing sequence labeling models mainly comprise HMM, MEMM and CRF. Taking the CRF model as an example, the keyword to be grouped may be used as an input, the probability values of the entities are output, and the entity with the highest probability value is determined as the entity part of speech of the keyword to be grouped.
(3) Identification of parts-of-speech in linguistics
The identification of the part of speech in linguistics is to identify whether the keyword to be grouped is a noun, a verb, an adjective, or the like.
The specific recognition mode can adopt a recognition mode defined by linguistics in the related art.
S22: information of existing groupings of advertiser accounts is determined.
The information of the existing packet includes: the part of speech of the existing keywords in the existing grouping and the attribute information of the existing grouping.
The part-of-speech recognition mode of the existing keyword is consistent with the part-of-speech recognition mode principle of the keyword to be grouped, and the part-of-speech recognition mode of the keyword to be grouped can be specifically carried out according to the part-of-speech recognition mode of the keyword to be grouped, and detailed description is omitted here.
The attribute information of the existing packet specifically includes: the category of the words stored in the existing groups, such as converting the attribute information of the word groups into effect words for storage.
The above describes the part-of-speech determination process, and the following describes the determination process of the predicted delivery effect. The method specifically comprises the following steps:
if the keywords to be grouped are the keywords which appear in history, counting the historical putting effect of the keywords to be grouped, and determining the predicted putting effect of the keywords to be grouped according to the counting result; or,
if the keywords to be grouped are keywords which do not appear in the history, determining the similar keywords of the keywords to be grouped which appear in the history, counting the historical putting effect of the similar keywords, and determining the predicted putting effect of the keywords to be grouped according to the counting result.
When the historical putting effect is counted, a statistical algorithm can be set according to the demand, for example, the average value of the historical conversion rate is used as the predicted putting effect.
When the similar keywords are determined, the similarity values of the keywords to be grouped and the historical keywords can be calculated, and the historical keywords with the highest similarity values are selected as the similar keywords. When calculating the similarity value between two words, for example, the two words may be converted into word vectors, and then the cosine distance equidistance value between the two word vectors is calculated, and the distance value is used as the similarity value between the two words.
S23: and determining the grouping matched with the keywords to be grouped in the existing grouping according to the part of speech and the predicted delivery effect of the keywords to be grouped and the information of the existing grouping.
In some examples, the grouping of the query words may be determined as a matching group according to the part of speech of the keyword to be grouped and the part of speech of the existing keyword, for example, the part of speech of the existing keyword in the query word grouping includes the member word, and the part of speech of the keyword to be grouped also includes the member word.
Further, if a keyword has multiple parts of speech, for example, the part of speech of a keyword includes conjunctive words (business parts of speech) and nouns (linguistic parts of speech), in this case, weights of different types of parts of speech may be set according to application requirements, and matching groups may be determined according to the weights and the corresponding parts of speech. For example, weights of a service part of speech, an entity part of speech and a linguistic part of speech may be set respectively, and then matching may be performed according to the part of speech with the highest weight, for example, the part of speech with the highest weight is the service part of speech, an existing keyword having a service part of speech consistent with a keyword to be grouped is found in each existing group, and the group where the existing keyword is located is taken as a matched group. Taking the above example of performing matching according to the highest weight, it is understood that weighting may also be performed according to the weight, and a specific weighting algorithm may be set according to the setting, and is not described in detail herein.
In some examples, the grouping may be performed according to the predicted delivery effect of the keyword to be grouped and the attribute information of the existing grouping, for example, if the predicted delivery effect of the keyword to be grouped satisfies a preset condition, the grouping for storing the effect word is used as a matched grouping, for example, if the predicted conversion rate of the keyword to be grouped is greater than a threshold value, the conversion word grouping is determined as a matched grouping.
S24: and dividing keywords to be grouped into matched groups.
For example, according to the part of speech, if the part of speech of the keyword to be grouped is consistent with the part of speech of the existing keyword in the group of query words, the keyword to be grouped is divided into the group of query words, or if the predicted delivery effect of the keyword to be grouped meets the preset condition, the keyword to be grouped is divided into the group of conversion words.
It is to be understood that the to-be-grouped keyword may be discarded if it does not match any of the existing groups.
In the embodiment, the information of the keywords to be grouped is determined, the groups matched with the keywords to be grouped are determined according to the information, and the keywords to be grouped are divided into the matched groups, so that the keywords can be automatically grouped without manual grouping, the efficiency and the accuracy are improved, and the cost is reduced. In this embodiment, accuracy can be improved by providing corresponding recognition modes for parts of speech of different types.
Fig. 3 is a schematic structural diagram of a keyword grouping apparatus according to an embodiment of the present application.
As shown in fig. 3, the apparatus 30 of the present embodiment includes: a first determination module 31, a second determination module 32 and a grouping module 33.
A first determining module 31, configured to determine information of the keywords to be grouped, where the information includes: part of speech and/or a predicted delivery effect;
a second determining module 32, configured to determine, according to the information, a group that is matched with the keyword to be grouped in an existing group;
and a grouping module 33, configured to divide the keywords to be grouped into the matched groups.
In some embodiments, referring to fig. 4, the first determination module, 31, comprises:
a first determining submodule 311 for determining parts of speech of the keywords to be grouped;
the first determining submodule 311 is specifically configured to:
when the part of speech includes a service part of speech, identifying the service part of speech of the keyword to be grouped based on a substring matching or classification model; or,
when the part of speech comprises an entity part of speech, identifying the entity part of speech of the keyword to be grouped based on a sequence tagging model; or,
and when the part of speech comprises a linguistic part of speech, identifying the linguistic part of speech of the keywords to be grouped based on the linguistic definition.
In some embodiments, referring to fig. 4, the first determining module 31 includes:
a second determining sub-module 312 for determining a predicted delivery effect of the keywords to be grouped;
the second determining sub-module 312 is specifically configured to:
if the keywords to be grouped are the keywords which appear in history, counting the historical putting effect of the keywords to be grouped, and determining the predicted putting effect of the keywords to be grouped according to the counting result; or,
if the keywords to be grouped are keywords which do not appear in the history, determining the similar keywords of the keywords to be grouped which appear in the history, counting the historical putting effect of the similar keywords, and determining the predicted putting effect of the keywords to be grouped according to the counting result.
In some embodiments, referring to fig. 4, the second determining module 32 includes:
the third determining submodule 321 is configured to, when the information includes a part of speech, identify a part of speech of an existing keyword in an existing group, determine a group in which the existing keyword whose part of speech is consistent with the part of speech of the keyword to be grouped is located, and determine the group as a group matched with the keyword to be grouped.
In some embodiments, referring to fig. 4, the second determining module 32 includes:
a fourth determining submodule 322, configured to determine, when the information includes a predicted delivery effect, a group for storing effect words in an existing group as a group matched with the keyword to be grouped when the predicted delivery effect reaches a preset condition.
It is understood that the apparatus of the present embodiment corresponds to the method embodiment described above, and specific contents may be referred to the related description of the method embodiment, and are not described in detail herein.
In the embodiment, the information of the keywords to be grouped is determined, the groups matched with the keywords to be grouped are determined according to the information, and the keywords to be grouped are divided into the matched groups, so that the keywords can be automatically grouped without manual grouping, the efficiency and the accuracy are improved, and the cost is reduced.
An embodiment of the present application further provides an apparatus, including: one or more processors; a memory for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to perform: determining information of keywords to be grouped, wherein the information comprises: part of speech and/or a predicted delivery effect; determining a group matched with the keywords to be grouped in the existing groups according to the information; and dividing the keywords to be grouped into the matched groups.
Embodiments of the present application also provide a non-transitory computer readable storage medium, where one or more programs, when executed by one or more processors of a device, cause the one or more processors to perform: determining information of keywords to be grouped, wherein the information comprises: part of speech and/or a predicted delivery effect; determining a group matched with the keywords to be grouped in the existing groups according to the information; and dividing the keywords to be grouped into the matched groups.
Embodiments of the present application also provide a computer program product, which when executed by one or more processors in a device causes the one or more processors to perform: determining information of keywords to be grouped, wherein the information comprises: part of speech and/or a predicted delivery effect; determining a group matched with the keywords to be grouped in the existing groups according to the information; and dividing the keywords to be grouped into the matched groups.
The above-mentioned device may be a server or a terminal device.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (11)
1. A keyword grouping method, comprising:
determining information of keywords to be grouped, wherein the information comprises: part of speech and/or a predicted delivery effect;
determining a group matched with the keywords to be grouped in the existing groups according to the information;
and dividing the keywords to be grouped into the matched groups.
2. The method of claim 1, wherein determining the part-of-speech of the keyword to be grouped comprises:
when the part of speech includes a service part of speech, identifying the service part of speech of the keyword to be grouped based on a substring matching or classification model; or,
when the part of speech comprises an entity part of speech, identifying the entity part of speech of the keyword to be grouped based on a sequence tagging model; or,
and when the part of speech comprises a linguistic part of speech, identifying the linguistic part of speech of the keywords to be grouped based on the linguistic definition.
3. The method of claim 1, wherein determining the predicted delivery effect of the keywords to be grouped comprises:
if the keywords to be grouped are the keywords which appear in history, counting the historical putting effect of the keywords to be grouped, and determining the predicted putting effect of the keywords to be grouped according to the counting result; or,
if the keywords to be grouped are keywords which do not appear in the history, determining the similar keywords of the keywords to be grouped which appear in the history, counting the historical putting effect of the similar keywords, and determining the predicted putting effect of the keywords to be grouped according to the counting result.
4. The method according to claim 1, wherein when the information includes part of speech, the determining, according to the information, the group matching the keyword to be grouped in the existing group comprises:
identifying the part of speech of the existing keywords in the existing grouping, grouping the existing keywords with the parts of speech consistent with the parts of speech of the keywords to be grouped, and determining the existing keywords to be the groups matched with the keywords to be grouped.
5. The method according to claim 1, wherein when the information includes a predicted delivery effect, the determining a group matching the keyword to be grouped in the existing group according to the information comprises:
and when the predicted putting effect reaches a preset condition, determining the group for storing the effect words in the existing group as the group matched with the keywords to be grouped.
6. A keyword grouping apparatus, comprising:
the first determining module is used for determining information of the keywords to be grouped, and the information comprises: part of speech and/or a predicted delivery effect;
the second determining module is used for determining the groups matched with the keywords to be grouped in the existing groups according to the information;
and the grouping module is used for dividing the keywords to be grouped into the matched groups.
7. The apparatus of claim 6, wherein the first determining module comprises:
a first determining submodule for determining the part of speech of the keywords to be grouped;
the first determination submodule is specifically configured to:
when the part of speech includes a service part of speech, identifying the service part of speech of the keyword to be grouped based on a substring matching or classification model; or,
when the part of speech comprises an entity part of speech, identifying the entity part of speech of the keyword to be grouped based on a sequence tagging model; or,
and when the part of speech comprises a linguistic part of speech, identifying the linguistic part of speech of the keywords to be grouped based on the linguistic definition.
8. The apparatus of claim 6, wherein the first determining module comprises:
a second determining submodule for determining a predicted delivery effect of the keywords to be grouped;
the second determining submodule is specifically configured to:
if the keywords to be grouped are the keywords which appear in history, counting the historical putting effect of the keywords to be grouped, and determining the predicted putting effect of the keywords to be grouped according to the counting result; or,
if the keywords to be grouped are keywords which do not appear in the history, determining the similar keywords of the keywords to be grouped which appear in the history, counting the historical putting effect of the similar keywords, and determining the predicted putting effect of the keywords to be grouped according to the counting result.
9. The apparatus of claim 6, wherein the second determining module comprises:
and the third determining submodule is used for identifying the part of speech of the existing keywords in the existing grouping when the information comprises the part of speech, and determining the grouping where the existing keywords with the parts of speech consistent with the parts of speech of the keywords to be grouped are located as the grouping matched with the keywords to be grouped.
10. The apparatus of claim 6, wherein the second determining module comprises:
and the fourth determining submodule is used for determining the groups for storing the effect words in the existing groups as the groups matched with the keywords to be grouped when the information comprises the predicted putting effect and the predicted putting effect reaches the preset condition.
11. An apparatus, comprising:
one or more processors; a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method recited in any of claims 1-5.
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| CN111782801A (en) * | 2019-05-17 | 2020-10-16 | 北京京东尚科信息技术有限公司 | Method and device for grouping keywords |
| CN112559895A (en) * | 2021-02-19 | 2021-03-26 | 深圳平安智汇企业信息管理有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112749546A (en) * | 2021-01-13 | 2021-05-04 | 叮当快药科技集团有限公司 | Retrieval matching processing method and device for medical semantics |
| CN114661917A (en) * | 2022-03-10 | 2022-06-24 | 深圳壹账通科技服务有限公司 | Text amplification method, system, computer device and readable storage medium |
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