CN112966192A - Region address naming method and device, electronic equipment and readable storage medium - Google Patents
Region address naming method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The disclosure discloses a region address naming method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers, in particular to the technical field of big data and natural language processing. The specific implementation scheme is as follows: performing word segmentation processing on the interest point names corresponding to the interest points in the interest point set to obtain an interest point name word segmentation result corresponding to the interest point names; labeling the regional words contained in the interest point names based on the interest point name word segmentation result to obtain an interest point name set; the interest point name set is a set of interest point name labeling results; determining regional keywords of the interest point name set by using a word frequency inverse document frequency algorithm based on the interest point name labeling result; and determining the address words of the target area based on the area keywords of the interest point name set. The method for naming the regional address can improve the accuracy and efficiency of naming the regional address.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for naming a local address, an electronic device, and a readable storage medium.
Background
The neighborhood is a basic unit of human activities and plays an important role in city management. The street names not only help people understand the history of the area, but also see through the functional type and geographical location of the street. In LBS (Location Based Services) application fields, such as driving, taking out, maps, etc., a neighborhood may be a recommended destination. Therefore, the accuracy of the street name has a great influence on the life of people.
Disclosure of Invention
The disclosure provides a region address naming method and device, electronic equipment and a readable storage medium.
According to a first aspect of the present disclosure, there is provided a region address naming method, including:
performing word segmentation processing on the interest point names corresponding to the interest points in the interest point set to obtain an interest point name word segmentation result corresponding to the interest point names; the interest point set is a set of interest points in a target area, and the interest point names comprise at least one area word;
labeling the regional words contained in the interest point names based on the interest point name word segmentation result to obtain an interest point name set; the interest point name set is a set of interest point name labeling results;
determining the regional keywords of the interest point name set by utilizing a word frequency inverse document frequency algorithm based on the interest point name labeling result;
and determining the address words of the target area based on the area keywords of the interest point name set.
According to a second aspect of the present disclosure, there is provided a region naming apparatus including:
the word segmentation module is used for carrying out word segmentation on the interest point names corresponding to the interest point sets in the interest point set to obtain the word segmentation results of the interest point names corresponding to the interest point names; wherein the interest point set is a set of interest points within a target region, and the interest point name includes at least one region word;
the labeling module is used for labeling the regional words contained in the interest point names based on the interest point name word segmentation results to obtain an interest point name set; the interest point name set is a set of interest point name labeling results;
the region keyword determining module is used for determining region keywords in the interest point name set based on the interest point name labeling result by utilizing a word frequency inverse document frequency algorithm;
and the area word determining module is used for determining the address words of the target area based on the area keywords of the interest point name set.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the region address naming methods.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of the area address naming methods.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of any of the above-mentioned area address naming methods.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flowchart of a method for naming a local address according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of determining regional keywords according to an embodiment of the present disclosure;
fig. 3 is a schematic block diagram of a region naming apparatus provided in an embodiment of the present disclosure;
FIG. 4 is a block diagram of a module for determining keywords in a region according to an embodiment of the disclosure;
FIG. 5 is a block diagram of an electronic device for implementing a region address naming method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
At present, the street names are mainly named by experts, and the naming mode has high accuracy, high naming cost and poor efficiency and coverage rate. Another way of naming a block is to take the name of a main POI (Point of Interest) as the block name by capturing the POI. Although the street naming mode is high in efficiency, the accuracy is poor, particularly when POI in the area are scattered, and names of the main POI and the secondary POI have large values, the determined street naming is often unreasonable.
In view of the above-mentioned problems in street naming, the present disclosure provides a naming method that can reasonably name a street where POIs are more dispersed.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flowchart of a method for naming a local address according to an embodiment of the present disclosure. As shown in fig. 1, the method for naming a region address includes:
step S101, performing word segmentation processing on the interest point names corresponding to the interest points in the interest point set to obtain the word segmentation results of the interest point names corresponding to the interest point names.
The Point of Interest (POI) is related to a geographic location, such as a geographic location Point represented by longitude and latitude, or may be used for labeling a sight spot, a train station, a house, and the like in a map carried by a map application program, so that the map application program can provide a location information service to a user according to the POI in the map. Such as a geographic location query service and a geographic location positioning service, etc.
The interest points can be labeled by using the interest point names, and the labeled content comprises at least one administrative level regional word in province, city, district and street, and can also comprise regional codes and subject words. The region code may be a street code, for example, a street number 5, and "number 5" is a code. The subject term is used for embodying the function of marking the position, such as the subject term of apartment, market, station, etc.
The set of points of interest is a set of individual points of interest within the target region. The target area is a geographical range covered in the map, and the covered geographical range is different in size. The target area comprises a plurality of interest points, and each interest point corresponds to one interest point name. And collecting the interest points in the target area together to obtain an interest point collection. The set of points of interest may be represented by "P", with each point of interest represented by "Pi"means.
The name of each point of interest in the point of interest set is described by an address name, the address name comprises at least one regional word of an administrative level, the regional word is a place name word for identifying an administrative area, and the administrative area can be provinces, cities, districts, streets and the like. For example, in the point of interest name "street 58 number of B area C of a city," a "is at the administrative level of city," B "is at the administrative level of" city area, "C" is at the administrative level of street, "58" is a region code, and the region code is a region word that does not belong to an administrative region.
In the embodiment of the disclosure, the interest point names corresponding to each interest point in the interest point set are subjected to word segmentation processing, and an interest point name word segmentation result corresponding to each interest point name is obtained. The word segmentation processing method may adopt any word segmentation method, which is not limited in this embodiment.
For example, the term is divided for the name of the point of interest, i.e. street 58 number of the B-city and the C-city, and the obtained term result of the name of the point of interest is: "city a", "district B", "street C", and "No. 58".
Step S102, labeling the regional words contained in the interest point names based on the interest point name word segmentation results, and obtaining an interest point name set.
Because the word segmentation result of each interest point name at least comprises one administrative level regional word, the word segmentation result of the interest point name needs to be labeled to obtain an interest point name labeling result, and the interest point name labeling results are collected together to obtain an interest point name collection. The means of labeling are not limited in this application.
And after the word segmentation result of each interest point name is labeled, obtaining an interest point name labeling result. For example, the interest point name "street 58 number of C in B area of city a" is labeled, and the obtained interest point name labeling result is "street 58 number (code) of C street (street) in B area of city a (city).
And step S103, determining the regional keywords of the interest point name set by using a word frequency inverse document frequency algorithm based on the interest point name labeling result.
Based on the labeling result of each interest point name in the interest point name set, performing word-frequency inverse document frequency (tf. idf) algorithm calculation on the region words, and determining region keywords in the interest point set. If the interest point name includes a plurality of regional words of administrative levels, the regional words of different levels can be respectively calculated to obtain regional keywords of corresponding levels.
In some embodiments, the frequency of the regional words of each administrative level in the interest point name set is respectively counted, the inverse document frequency of the regional words of the administrative level in the interest point name set is calculated, the high-weight word frequency inverse document frequency is obtained, and the regional word with the maximum word frequency inverse document frequency is used as the regional keyword corresponding to the administrative level.
The word frequency is the number of times that the target word appears in the interest point name/the total word number of the document. The inverse document frequency is log (total number of interest point names/(number of interest point names where the target word appears + 1)). The word frequency inverse document frequency is the word frequency x the inverse document frequency.
For example, in calculating a region keyword with an administrative level of "street," the word frequency tf (street of ' C ') -cnt ({ d ')i|street(di) 'C street' }), where cnt () represents a statistical function, diIndicating an address, street (d)i) Representation acquisition diCorresponding to street, inverse document frequency
Wherein cnt () represents a statistical function, AiIndicating the region, idf _ street (A)i) Indicates the acquisition area AiThe street word of (2).
In this embodiment, the regional words corresponding to each administrative level are respectively calculated through the word frequency and the inverse document frequency, and the regional word with the highest frequency is selected as the regional keyword of the administrative level.
For example, the set of interest point names includes fifty interest point names, and each interest point name includes a regional word with an administrative level of street. The names of the streets comprise 'C street', 'D street' and 'E street', and the names are ranked from high to low according to the word frequency inverse document frequency to be 'C street', 'D street' and 'E street', then the 'C street' is determined as the regional key word of the administrative level street in the interest point name set, namely the regional key word of the target region corresponding to the interest point name set is determined.
Step S104, determining the address words of the target area based on the area keywords in the interest point name set.
After the regional keywords of each administrative level in the interest point name set are obtained, the regional keywords are arranged according to the administrative levels from high to low, the address words of the interest point name set are obtained, and therefore the address words of the target region are obtained. Wherein the address words comprise at least one administrative level.
In the case where the highest administrative level in the interest point set is "street", only "street" is included in the address word of the corresponding target area. In the case that the highest administrative level in the interest point set is "area", two administrative levels of "area" and "street" should be included in the address word of the corresponding target area. In the case that the highest administrative level in the interest point set is "city", three administrative levels of "city", "district", and "street" should be included in the address word of the corresponding target area. By analogy, four or more administrative levels may also be included.
The regional address naming method provided by the embodiment of the disclosure is based on the interest point names in the interest point name set, and determines the address words of the target region corresponding to the interest point name set by using a word frequency inverse document frequency algorithm, so that the address words are more accurate, and the efficiency of regional naming can be improved.
In some embodiments, as shown in fig. 2, step S103, determining the regional keywords in the interest point name set based on the interest point name tagging result and by using a word frequency inverse document frequency algorithm, includes:
step S201, extracting area words from all interest point name labeling results.
When the interest point names comprise a plurality of administrative level regional words, extracting the administrative level regional words from each interest point name to obtain all administrative level regional words in all the interest point names in the interest point set.
Step S202, calculating the word frequency inverse document frequency of the regional words in the interest point name set.
In some embodiments, the word frequency inverse document frequency of the region words in the set of point of interest names is calculated. When a plurality of administrative-level regional words exist in the interest point names, respectively calculating the word frequency inverse document frequency of the regional words of each administrative level contained in the interest point set, and obtaining the word frequency inverse document frequency of the regional words of different administrative levels. For the same administrative level, a plurality of corresponding regional words may exist, and each regional word can obtain the corresponding word frequency and inverse document frequency.
Step S203, determining the regional keywords of the interest point name set based on the word frequency inverse document frequency of the regional words.
And determining the regional keywords of the interest point name set based on the word frequency inverse document frequency of the regional words. When a plurality of regional words of administrative levels exist in the interest point name, the regional word with the maximum word frequency and the maximum document frequency can be selected from all levels of administrative levels as the regional keyword corresponding to the administrative levels. It should be noted that the area keywords of the interest point name set are consistent with the area keywords of the interest point set, and when the area keywords of the interest point name set are determined, the area keywords of the interest point set can be determined.
After the regional keywords of each level of administrative levels are determined, the administrative levels are arranged from high to low, and the regional keywords of each level of administrative levels in the interest point name set are determined.
For example, the administrative levels included in the interest point name set include "province", "city", "region" and "street", and it is determined that the number of interest point names in which "province" appears in the interest point name set is less than a preset number, it may be determined that the administrative levels in the interest point name set should include "city", "region" and "street", so that the region keywords corresponding to "city", "region" and "street" are determined as the region keywords of each administrative level in the interest point name set.
After the regional keywords are determined, the regional keywords of the interest point name set are determined based on the regional keywords, and the regional keywords which do not belong to the interest point name set are removed, so that the address words of the interest point name set are more accurate.
In some embodiments, the target region includes not only the address word but also the region code, and therefore, the region code of the target region needs to be determined from the interest point set.
In step S104, after determining the address word of the target area based on the area keyword of the interest point set, determining the area code of the target area and determining the address name of the target area. In the disclosed embodiment, the address name includes an address word and a region code.
In some embodiments, the region code is obtained from all the interest point names which are the same as the address words of the target region; aggregating the regional codes to obtain a regional code aggregation result; determining a region code of the target region based on the region code aggregation result; the address name of the target area is determined based on the area code of the target area and the address words of the target area.
After the address words of the target area are determined, area codes are extracted from all the interest point names matched with the address words in the interest point name set, and the extracted area codes are aggregated to obtain an area code aggregation result. When the region codes are more dispersed, the maximum value and the minimum value in the region codes can be selected to determine an interval value.
In some embodiments, the region code for the target region is determined based on the region code aggregation result, e.g., when the determined interval value is 20-58, the region code is street number 20-58.
In the present embodiment, the area code based on the target area and the address word of the target area are combined together, thereby determining the address name of the target area. For example, when the street in which the target area is determined is coded as street number 20-58, and the address word of the target area is "street number C, City B", the address name of the target area is determined as "street number 20-58, City B, district C".
In the embodiment of the disclosure, the region codes of the target region are obtained by aggregating the region codes, and the address name of the target region is determined based on the region codes of the target region and the address words of the target region, so that the address name of the target region is more accurate and detailed.
In some embodiments, the target area also needs to determine the subject term, i.e., the function of the target area. For example, the target area functions as an apartment, hospital, or mall. In order to determine the subject word of the target area, after determining the address name of the target area based on the area code of the target area and the address word of the target area, the method further comprises:
extracting the interest point name matched with the address name of the target area from the interest point name set; acquiring candidate function words in the interest point names matched with the address names of the target areas; determining a subject function word corresponding to the address name of the target area by using a word frequency inverse document frequency algorithm based on the candidate function word; and obtaining the address name of the target area with the functional attribute according to the address name and the subject term of the target area.
In some embodiments, the interest point names matched with the address names of the target area are extracted from the interest point name set, the interest point names which obviously do not belong to the target area are excluded, and interference is reduced, so that the accuracy of the subject term is improved.
And acquiring the candidate function words of the target area from the interest point names matched with the address names of the target area. And determining the subject function words corresponding to the address names of the target areas by using the word frequency inverse document frequency based on the candidate function words.
For example, the candidate function words obtained from the point of interest names in the point of interest name set are "apartment" and "hospital", and the word frequency inverse document frequencies of "apartment" and "hospital" are calculated, respectively, tf (apartment') ═ sum ({ p) }i×wi|func(pi) An apartment' }). Wherein p isiRepresenting the name of the point of interest, the sum () function is used to obtain the statistical value, func (p)i) Represents piCorresponding functional region fi，wiIndicating the degree of hotness of the access or search. And then determining the subject function words of the target area based on the word frequency inverse document frequency of the apartment and the hospital.
In some embodiments, if the target region includes a plurality of candidate functional words, and the functional words corresponding to different region codes are different, the functional words corresponding to different region codes, that is, the topic functional words corresponding to the address names of the target region, may be determined respectively by the word frequency inverse document frequency.
And combining the address name of the target area with the functional attribute by the subject word. For example, if the address name of the target area is "20-58 # street C in B zone of city a", the subject word corresponding to the address name is "apartment", and the finally obtained address name of the target area is "20-58 # apartment street C in B zone of city a".
In the embodiment of the disclosure, the topic function word of the target area is determined by using the word frequency inverse document frequency, so that not only can an accurate topic function word be obtained, but also the perspective of the address name of the target area is improved, not only the address information of the target area can be obtained through the address name, but also the function type of the area can be obtained, and the related information of the target area is better expressed.
In some embodiments, in step S101, before performing word segmentation on the interest point name corresponding to the interest point in the interest point set and obtaining a word segmentation result of the interest point name corresponding to the interest point name, the method further includes:
screening the preset area based on the interest points in the preset area to determine a target area; and obtaining the interest point set by using the interest points in the target area.
The preset area is an administrative area which is set by a user and needs to determine the address name.
In some embodiments, all points of interest within the predetermined area are obtained via a public database. The interest points are marked in the map or recommended in the using process of the user. The screening mode of the preset area can be selected by heat degree, or by longitude and latitude and other modes. The hot degree refers to the retrieval hot degree or the access hot degree.
In the embodiment of the disclosure, the preset region is screened based on the interest point to determine the target region suitable for the region naming provided by the embodiment, so that the accuracy of the target region naming is improved, meanwhile, the preset region in which the region name can be determined without the region naming method provided by the embodiment can be excluded, and the efficiency of region naming is improved.
In some embodiments, the target region is determined by heat-filtered points of interest, the filtering step comprising:
obtaining a heat entropy of a preset area based on the heat of interest points contained in the preset area; and determining a preset area with the heat entropy larger than a preset heat entropy threshold value as a target area.
In some embodiments, all interest points in the preset area a and the heat of each interest point are obtained from the published travel taxi taking data, the heat entropy of the preset area is calculated according to the heat of the interest points, and the preset area with the heat entropy larger than a preset heat entropy threshold value θ is determined as the target area.
The heat entropy of the preset area can be obtained by the following calculation method.
Wherein H (x) represents the heat entropy of the predetermined region, wiAnd i represents the sequence number of the interest point in the preset area. Wherein, the sequence numbers of the interest points can be randomly arranged.
In some embodiments, if the heat entropy of the preset region is smaller than the preset heat entropy threshold θ, it is indicated that the interest points of the preset region are concentrated. If the heat entropy of the preset area is larger than or equal to the preset heat entropy threshold value theta, the interest points of the preset area are dispersed. For the area with concentrated interest points of the preset area, the area name is easy to determine, and the name of the preset area can be directly determined. For areas where the points of interest in the preset area are relatively dispersed, the area name may be determined by using the area naming method provided by the embodiment of the present disclosure.
According to the method and the device, the heat entropy of the preset area is determined through the heat of the interest point, the target area is determined based on the heat entropy, and the screening of the target area is more accurate.
In some embodiments, obtaining the heat entropy of the preset region based on the heat of each interest point in the preset region includes: acquiring the heat of interest points in a preset area; normalizing the heat of the interest points in the preset area; and obtaining the heat entropy of the preset area based on the heat of the interest point after the normalization processing.
The data normalization problem is an important problem in feature vector expression in data mining, when different features are listed together, small data on absolute numerical values are 'eaten' by big data due to the expression mode of the features, and all that is needed is to perform normalization processing on extracted feature vectors to ensure that each feature is treated equally.
According to the embodiment of the disclosure, after normalization processing is performed on the heat of each interest point in the preset area, the heat entropy of the preset area is determined, so that the heat entropy is more accurate, and the target area is more accurately determined.
In a second aspect, the present disclosure further provides a region naming apparatus, which can reasonably name POI more distributed blocks.
Fig. 3 is a schematic block diagram of a region naming apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the region naming apparatus 300 includes:
the word segmentation module 301 is configured to perform word segmentation on the interest point names corresponding to the interest points in the interest point set, and obtain a word segmentation result of the interest point names corresponding to the interest point names.
A Point of Interest (POI) is related to a geographic location, such as a geographic location Point represented by longitude and latitude, or may be used for labeling a sight spot, a train station, a house, and the like in a map carried by a map application program, so that the map application program can provide a location information service to a user according to the POI in the map. Such as a geographic location query service and a geographic location positioning service, etc.
And labeling the name of the point of interest, wherein the labeled content comprises at least one regional word in administrative levels of province, city, district and street, and can also comprise a regional code and a subject word. The region code may be a street code, for example, a street number 5, and "number 5" is a code. The subject term is used for embodying the function of marking the position, such as the subject term of apartment, market, station, etc.
The set of points of interest is the eyeA set of points of interest within the target area. The target area is a geographical range covered in the map, and the covered geographical range is different in size. The target area comprises a plurality of interest points, and each interest point corresponds to one interest point name. And collecting the interest points in the target area together to obtain an interest point collection. The set of points of interest may be represented by "P", with each point of interest represented by "Pi"means.
The name of each point of interest in the point of interest set is described by an address name, the address name comprises at least one regional word of an administrative level, the regional word is a place name word for identifying an administrative area, and the administrative area can be provinces, cities, districts, streets and the like. For example, in the point of interest name "street 58 number of B area C of a city," a "is at the administrative level of city," B "is at the administrative level of" city area, "C" is at the administrative level of street, "58" is a region code, and the region code is a region word that does not belong to an administrative region.
In the embodiment of the disclosure, the interest point names corresponding to each interest point in the interest point set are subjected to word segmentation processing, and an interest point name word segmentation result corresponding to each interest point name is obtained. The word segmentation processing method may adopt any word segmentation method, which is not limited in this embodiment.
For example, the term is divided for the interest point name a, city, B, C, street 58, and the obtained term result of the interest point name is: "city a", "district B", "street C", and "No. 58".
The labeling module 302 is configured to label, based on the interest point name word segmentation result, regional words at each administrative level included in the interest point name to obtain an interest point name set.
Because the word segmentation result of each interest point name at least comprises one administrative level regional word, the word segmentation result of the interest point name needs to be labeled to obtain an interest point name labeling result, and the interest point name labeling results are aggregated to obtain an interest point name set. The means of labeling are not limited in this application.
And the region keyword determining module 303 is configured to determine a region keyword in the interest point name set based on the interest point name tagging result and by using the word frequency inverse document frequency.
And performing word frequency inverse document frequency (tf. idf) calculation on the regional words based on the labeling result of each interest point name in the interest point name set, and determining regional keywords in the interest point set. If the interest point name includes a plurality of regional words of administrative levels, the regional words of different levels can be respectively calculated to obtain regional keywords of corresponding levels.
In some embodiments, the frequency of the regional words of each administrative level in the interest point name set is respectively counted, the inverse document frequency of the regional words of the administrative level in the interest point name set is calculated, the high-weight word frequency inverse document frequency is obtained, and the regional word with the maximum word frequency inverse document frequency is used as the regional keyword corresponding to the administrative level.
The word frequency is the number of times that the target word appears in the interest point name/the total word number of the document. The inverse document frequency is log (total number of interest point names/(number of interest point names where the target word appears + 1)). The word frequency inverse document frequency is the word frequency x the inverse document frequency.
For example, in calculating a region keyword with an administrative level of "street," the word frequency tf (street of ' C ') -cnt ({ d ')i|street(di) 'C street' }), where cnt () represents a statistical function, diIndicating an address, street (d)i) Representation acquisition diCorresponding to street, inverse document frequency
Wherein cnt () represents a statistical function, AiIndicating the address, idf _ street (A)i) Indicates the acquisition area AiThe street word of (2).
In this embodiment, the regional words corresponding to each administrative level are respectively calculated through the word frequency and the inverse document frequency, and the regional word with the highest frequency is selected as the regional keyword of the administrative level.
For example, the set of interest point names includes fifty interest point names, and each interest point name includes a regional word with an administrative level of street. The names of the streets comprise 'C street', 'D street' and 'E street', and the names are ranked from high to low according to the word frequency inverse document frequency to be 'C street', 'D street' and 'E street', then the 'C street' is determined as the regional key word of the administrative level street in the interest point name set, namely the regional key word of the target region corresponding to the interest point name set is determined.
And the regional word determining module 304 is configured to determine address words of the target region based on the regional keywords in the interest point name set.
After the regional keywords of each administrative level in the interest point name set are obtained, the regional keywords are arranged according to the administrative levels from high to low, the address words of the interest point name set are obtained, and therefore the address words of the target region are obtained. Wherein the address words comprise at least one administrative level.
In the case where the highest administrative level in the interest point set is "street", only "street" is included in the address word of the corresponding target area. In the case that the highest administrative level in the interest point set is "area", two administrative levels of "area" and "street" should be included in the address word of the corresponding target area. In the case that the highest administrative level in the interest point set is "city", three administrative levels of "city", "district", and "street" should be included in the address word of the corresponding target area. By analogy, four or more administrative levels may also be included.
The region address naming method provided by the embodiment of the disclosure is based on the interest point names in the interest point name set, and determines the address words of the target region corresponding to the interest point name set by using the word frequency inverse document frequency, so that the address words are more accurate, and the region naming efficiency can be improved.
In some embodiments, as shown in fig. 4, the region keyword determination module 400 includes:
an extracting unit 401, configured to extract the area words from all the interest point name labeling results.
When the interest point names comprise a plurality of administrative level regional words, extracting the administrative level regional words from each interest point name to obtain all the interest point names in the interest point set and all the administrative level regional words.
And the calculating unit 402 is configured to calculate a word frequency inverse document frequency of the regional words at each administrative level.
In some embodiments, the calculation unit 402 calculates the word frequency inverse document frequency of the region words in the set of point of interest names. When there are a plurality of administrative levels of regional words in the interest point name, the calculating unit 402 calculates the word frequency inverse document frequency of each level of administrative level of regional words, and obtains the word frequency inverse document frequency of different administrative levels of regional words. For the same administrative level, a plurality of corresponding regional words may exist, and each regional word can obtain the corresponding word frequency and inverse document frequency.
The determining unit 403 determines the regional keywords in the interest point name set based on the word frequency inverse document frequency of the regional words.
And determining the regional keywords of the interest point name set based on the word frequency inverse document frequency of the regional words. When a plurality of regional words of administrative levels exist in the interest point name, the regional word with the maximum word frequency and the maximum document frequency can be selected from all levels of administrative levels as the regional keyword corresponding to the administrative levels. It should be noted that the area keywords of the interest point name set are consistent with the area keywords of the interest point set, and when the area keywords of the interest point name set are determined, the area keywords of the interest point set can be determined.
After determining the regional keywords of different administrative levels, arranging the administrative levels in a sequence from high to low, and determining the regional keywords of each administrative level in the interest point name set.
For example, the administrative levels included in the interest point name set include "province", "city", "region" and "street", and it is determined that the number of interest point names in which "province" appears in the interest point name set is less than a preset number, it may be determined that the administrative levels in the interest point name set should include "city", "region" and "street", so that the region keywords corresponding to "city", "region" and "street" are determined as the region keywords of each administrative level in the interest point name set.
After the regional keywords are determined, the regional keywords of the interest point name set are determined based on the regional keywords, and the regional keywords which do not belong to the interest point name set are removed, so that the address words of the interest point name set are more accurate.
In some embodiments, the region naming apparatus further comprises:
and the acquisition module is used for acquiring the area codes from all the interest point names which are the same as the address words of the target area.
And the aggregation module is used for aggregating the region codes to obtain a region code aggregation result.
And the region code determining module is used for determining the region code of the target region based on the region code aggregation result.
And the address name determining module is also used for determining the address name of the target area based on the area code of the target area and the address words of the target area.
After the address words of the target area are determined, area codes are extracted from all the interest point names matched with the address words in the interest point name set, and the extracted area codes are aggregated to obtain an area code aggregation result. When the region codes are more dispersed, the maximum value and the minimum value in the region codes can be selected to determine an interval value.
In some embodiments, the region code for the target region is determined based on the region code aggregation result, e.g., when the determined interval value is 20-58, the region code is street number 20-58.
In the present embodiment, the area code based on the target area and the address word of the target area are combined together, thereby determining the address name of the target area. For example, when the street in which the target area is determined is coded as street number 20-58, and the address word of the target area is "street number C, City B", the address name of the target area is determined as "street number 20-58, City B, district C".
In some embodiments, the region naming apparatus further comprises:
and the extraction module is used for extracting the interest point name matched with the address word of the target area from the interest point name set.
And the functional word acquisition module is used for acquiring functional words in the interest point names matched with the address words of the target area.
And the subject word determining module is used for determining the functional words corresponding to the address words of the target area by using a word frequency inverse document frequency algorithm based on the functional words and taking the functional words corresponding to the address words of the target area as the subject words.
And the address name determining module is also used for obtaining the address name of the target area with the function attribute according to the address words and the subject words of the target area.
In some embodiments, the interest point names matched with the address names of the target area are extracted from the interest point name set, the interest point names which obviously do not belong to the target area are excluded, and interference is reduced, so that the accuracy of the subject term is improved.
And acquiring the candidate function words of the target area from the interest point names matched with the address names of the target area. And determining the subject function words corresponding to the address names of the target areas by using the word frequency inverse document frequency based on the candidate function words.
For example, the candidate function words obtained from the point of interest names in the point of interest name set are "apartment" and "hospital", and the word frequency inverse document frequencies of "apartment" and "hospital" are calculated, respectively, tf (apartment') ═ sum ({ p) }i×wi|func(pi) An apartment' }). Wherein p isiRepresenting the name of the point of interest, the sum () function is used to obtain the statistical value, func (p)i) Represents piCorresponding functional region fi，wiIndicating the degree of hotness of the access or search. And then determining the subject function words of the target area based on the word frequency inverse document frequency of the apartment and the hospital.
In some embodiments, if the target region includes a plurality of candidate functional words, and the functional words corresponding to different region codes are different, the functional words corresponding to different region codes, that is, the topic functional words corresponding to the address names of the target region, may be determined respectively by the word frequency inverse document frequency.
And combining the address name of the target area with the functional attribute by the subject word. For example, if the address name of the target area is "20-58 # street C in B zone of city a", the subject word corresponding to the address name is "apartment", and the finally obtained address name of the target area is "20-58 # apartment street C in B zone of city a".
In the embodiment of the disclosure, the topic function word of the target area is determined by using the word frequency inverse document frequency, so that not only can an accurate topic function word be obtained, but also the perspective of the address name of the target area is improved, not only the address information of the target area can be obtained through the address name, but also the function type of the area can be obtained, and the related information of the target area is better expressed.
In some embodiments, the region naming apparatus further comprises: the screening module is used for screening the preset area based on the interest points and determining a target area; and the set determining module is used for obtaining the interest point set by using the interest points in the target area.
In some embodiments, a screening module, comprising: the heat entropy acquiring unit is used for acquiring the heat entropy of the interest points in the preset area based on the heat of each interest point in the preset area; and the target area determining unit is used for determining a preset area with the heat entropy of the interest point larger than a preset heat entropy threshold as the target area.
In some embodiments, the heat entropy obtaining unit includes: the heat obtaining subunit is used for obtaining the heat of each interest point in the preset area; the processing subunit is used for carrying out normalization processing on the heat degree of each interest point in the preset area; and the heat entropy obtaining subunit is used for obtaining the interest point heat entropy of the preset area based on the heat of the interest point after the normalization processing.
The region naming device provided by the embodiment determines the address words of the interest points and the corresponding target regions based on the names of the interest points in the interest point set and by using the word frequency inverse document frequency, so that the address words are more accurate, and the region naming efficiency can be improved.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the area address naming method. For example, in some embodiments, the region address naming method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When a computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the above described method of region address naming may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the region address naming method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to an embodiment of the present disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements any one of the above-mentioned region address naming methods.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (12)
1. A region address naming method comprises the following steps:
performing word segmentation processing on the interest point names corresponding to the interest points in the interest point set to obtain an interest point name word segmentation result corresponding to the interest point names; the interest point set is a set of interest points in a target area, and the interest point names comprise at least one area word;
labeling the regional words contained in the interest point names based on the interest point name word segmentation result to obtain an interest point name set; the interest point name set is a set of interest point name labeling results;
determining the regional keywords of the interest point name set by utilizing a word frequency inverse document frequency algorithm based on the interest point name labeling result;
and determining the address words of the target area based on the area keywords of the interest point name set.
2. The method of claim 1, wherein the determining the region keywords of the interest point name set based on the interest point name labeling result by using a word frequency inverse document frequency algorithm comprises:
extracting the area words from all the interest point name labeling results;
calculating the word frequency inverse document frequency of the regional words in the interest point name set;
and determining the regional keywords of the interest point name set based on the word frequency inverse document frequency of the regional words.
3. The method of claim 1, wherein after determining the address word of the target area based on the area keyword of the set of interest point names, further comprising:
acquiring region codes from all the interest point names which are the same as the address words of the target region;
aggregating the region codes to obtain a region code aggregation result;
determining a region code of the target region based on the region code aggregation result;
determining an address name of the target area based on the area code of the target area and the address word of the target area.
4. The method of claim 3, wherein after determining the address name of the target region based on the region code of the target region and the address word of the target region, further comprising:
extracting the interest point name matched with the address name of the target area from the interest point name set;
acquiring candidate function words in the interest point names matched with the address names of the target areas;
determining a subject function word corresponding to the address name of the target area by using a word frequency inverse document frequency algorithm based on the candidate function word;
and obtaining the address name of the target area with the function words according to the address name of the target area and the subject function words.
5. The method according to any one of claims 1 to 4, wherein before performing a word segmentation process on the interest point names corresponding to the interest points in the interest point set and obtaining a word segmentation result of the interest point names corresponding to the interest point names, the method further comprises:
screening a preset area based on an interest point in the preset area to determine a target area;
and obtaining the interest point set by using the interest points in the target area.
6. The method of claim 5, wherein the screening the preset region based on the interest points in the preset region to determine a target region comprises:
obtaining a heat entropy of the preset area based on the heat of the interest point in the preset area;
and determining the preset area with the heat entropy larger than a preset heat entropy threshold value as the target area.
7. The method of claim 6, wherein the heat comprises any one of: the retrieval heat and the access heat.
8. The method of claim 6, wherein the obtaining the heat entropy of the preset region based on the heat of each of the interest points in the preset region comprises:
acquiring the heat of the interest points in the preset area;
normalizing the heat of the interest points in the preset area;
and obtaining the heat entropy of the preset area based on the heat of the interest point after the normalization processing.
9. A region naming apparatus comprising:
the word segmentation module is used for carrying out word segmentation on the interest point names corresponding to the interest point sets in the interest point set to obtain the word segmentation results of the interest point names corresponding to the interest point names; wherein the interest point set is a set of interest points within a target region, and the interest point name includes at least one region word;
the labeling module is used for labeling the regional words contained in the interest point names based on the interest point name word segmentation results to obtain an interest point name set; the interest point name set is a set of interest point name labeling results;
the region keyword determining module is used for determining region keywords in the interest point name set based on the interest point name labeling result by utilizing a word frequency inverse document frequency algorithm;
and the area word determining module is used for determining the address words of the target area based on the area keywords of the interest point name set.
10. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
11. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
12. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
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