A method, device, equipment and medium for determining target text label

Abstract

The present application relates to the technical field of text label processing, and in particular to a method for determining a target text label, the method comprising the following steps: first, obtaining the text priority of each initial text and obtaining a number of initial texts corresponding to each key text label, then obtaining a second text label score corresponding to each key text label, and determining the second text label score that is not less than the target label score as the target text label; it can be seen that the present invention combines the importance of the text and the usage rate of the label to filter out the text label, which can reflect the importance and effectiveness of the text label, and then filter out more valuable text labels.

Description

Technical Field

[0001] The present invention relates to the technical field of text label processing, and in particular to a method, device, equipment and medium for determining a target text label. Background Art

[0002] To facilitate text analysis and processing, the labeling systems of different text storage platforms usually have their own set standards, while high-level service platforms usually have unified classification standards. Several low-level service platforms will build labeling systems according to the unified classification standards and add customized decentralized labels based on business needs or regional differences. Among these decentralized labels, there are situations where the usage value is different. Therefore, it is very meaningful to screen out high-value labels from several decentralized labels to optimize the unified labeling system.

[0003] At present, when screening high-value tags, the tags in the existing tag system are usually screened through a text database, and the importance of the tag is determined based on the usage of the tag, without taking into account the actual situation of the text. The reliability of the screened tags is not high. For example, a patent document with application number "2022110849118" and name "A real estate industry tag system optimization method, system, device and storage medium" proposes a real estate industry tag system optimization method, which implements tag screening according to tag optimization parameters and set tag optimization thresholds. When determining the tag optimization parameters, it is extended only according to the number of hits of the leaf node for the text. The screening direction is relatively single, and the reliability of high-value tag screening needs to be improved. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to a first aspect of the present invention, a method for determining a target text label is provided, the method comprising the following steps:

[0006] S100, obtaining the text priority of each initial text according to the content data information of each initial text among several pre-stored initial texts; the content data information of the initial text includes several keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in several initial texts and the creation time of the initial text itself.

[0007] S200 , labeling each initial text based on a plurality of preset key text labels, and obtaining a plurality of initial texts corresponding to each key text label.

[0008] S300 , obtaining a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0009] S400: When there is a second text tag whose score is not less than the preset target tag score, a key text tag corresponding to the second text tag whose score is not less than the preset target tag score is determined as the target text tag.

[0010] According to a second aspect of the present invention, a device for determining a target text label is provided, the device comprising:

[0011] The first acquisition module is used to obtain the text priority of each initial text based on the content data information of each initial text in a number of pre-stored initial texts; the content data information of the initial text includes a number of keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in a number of initial texts and the creation time of the initial text itself.

[0012] The second acquisition module is used to label each initial text based on a plurality of preset key text labels, and acquire a plurality of initial texts corresponding to each key text label.

[0013] The third acquisition module is used to acquire the second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0014] The first determination module is configured to determine, when there is a second text tag score that is not less than a preset target tag score, a key text tag corresponding to the second text tag score that is not less than the preset target tag score as a target text tag.

[0015] According to a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned target text label determination method when executing the computer program.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the target text label determination method described above is implemented.

[0017] Compared with the prior art, the present invention has obvious beneficial effects. By means of the above technical solution, a target text label determination method provided by the present invention can achieve considerable technical advancement and practicality, and has wide industrial utilization value, and has at least the following beneficial effects:

[0018] The present invention provides a method for determining a target text label. First, the text priority of an initial text is calculated based on multiple content data information of the initial text. Then, a second text label score corresponding to each key text label is obtained based on the text priority and the number of initial texts corresponding to each key text label. When calculating the score, the importance of different texts is taken into account on the basis of the number of texts covered by each key text label, which is conducive to screening out more reliable and effective labels. Finally, when the second text label score is not less than a preset target label score, the key text label corresponding to the second text label score is determined as the target text label. The target label score is calculated based on the text priority and the first text label, so that the formulation of the target label score is more reasonable, which is conducive to screening out reasonable and effective text labels. In summary, the present invention can screen out target text labels in combination with the importance of the text and the usage of the label, reflecting the importance and effectiveness of the target text label, making the screened text labels more valuable, which is conducive to the expansion and optimization of the unified label system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a method for determining a target text label provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0023] Embodiment 1

[0024] This embodiment provides a method for determining a target text label. Figure 1 As shown, the method comprises the following steps:

[0025] S100, obtaining the text priority of each initial text according to the content data information of each initial text among several pre-stored initial texts; the content data information of the initial text includes several keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in several initial texts and the creation time of the initial text itself.

[0026] Specifically, the several keywords in the initial text refer to several keywords obtained from the initial text through a keyword extraction model.

[0027] Specifically, the text priority of the initial text meets the following conditions:

[0028] W=ε1×(K1 / K2)+ε2×(K2 / K3)+ε3×(1 / K4), where W refers to the text priority of the corresponding initial text, K1 is the total number of characters of several keywords in the corresponding initial text, K2 is the total number of characters of the corresponding initial text, K3 is the average number of characters of several initial texts, K4 is the difference between the current time and the creation time of the corresponding initial text, ε1 is the preset first weight coefficient, ε2 is the preset second weight coefficient, and ε3 is the preset third weight coefficient.

[0029] Specifically, the method further provides a first service platform and several second service platforms, the first service platform is higher than each second service platform, and any two second service platforms have the same level; the first service platform is in communication connection with each second service platform.

[0030] Specifically, the first service platform is configured with a plurality of first text tags; the first text tags are used to determine the target tag score; that is, the first service platform stores a first text tag set B={B1, B2, ..., B j , ..., B n}, where B j It refers to the jth first text label, j=1, 2, ..., n, where n is the number of first text labels.

[0031] Specifically, the plurality of key text tags refers to the sum of the key text tags obtained from the given plurality of second service platforms; that is, the second storage medium stores a key text tag set TA={TA1, TA2, ..., TA a ,……,TA c}, where TA a It refers to the ath key text label, a=1, 2, ..., c, c is the number of key text labels.

[0032] Specifically, the several initial texts refer to the sum of the initial texts obtained from the given several second service platforms; that is, the first storage medium stores the initial text list set TE={TE1, TE2, . . . , TE r ,……,TE g}, where TE r It refers to the rth initial text, r=1, 2,…, g, where g is the number of initial texts.

[0033] Specifically, the initial text list set includes m initial text lists, the initial text lists correspond to the second service platforms one by one, and each of the initial text lists includes several initial texts; it can be understood that: the m initial text lists are respectively obtained from the m second service platforms.

[0034] In a specific embodiment, the key text label is obtained by the following steps:

[0035] S001, input each initial text into several second service platforms for labeling, and obtain the second tag usage frequency of each second text tag in each second service platform; the second text tag is any text tag pre-configured in any second service platform.

[0036] The specific steps are: input each initial text in TE into m second service platforms for labeling, and obtain the second label usage frequency list set F corresponding to the initial text list set 2 ={F 2 1, F 2 2, ..., F2 i , ..., F 2 m}, F 2 i ={F 2 i1 , F 2 i2 , ..., F 2 iu , ..., F 2 iq(i)}, F 2 iu ={F 2 iu1 , F 2 iu2 , ..., F 2 iux , ..., F 2 ium}, where F 2 iux It refers to the frequency of use of the u-th second text tag in the i-th second text tag set in the x-th initial text list, where u=1, 2, ..., q(i), q(i) is the number of second text tags in the i-th second text tag set, i=1, 2, ..., m, and x=1, 2, ..., m; the i-th second text tag set refers to a set of several text tags corresponding to the i-th second service platform.

[0037] Specifically, F 2 iux Meet the following conditions:

[0038] F 2 iux =L 2 iux / L 2 x , where L 2 iux is the number of times the uth second text label in the ith second text label set is used in the xth initial text list, L 2 x is the number of initial texts in the xth initial text list.

[0039] S002, according to the preset target tag usage frequency, respectively select a plurality of third tag usage frequencies corresponding to each second text tag from a plurality of second tag usage frequencies; the third tag usage frequency refers to any second tag usage frequency greater than the target tag usage frequency among the plurality of second tag usage frequencies corresponding to the second text tag; for example: for F 2 iu , from F2 iu Get the usage frequencies of y third tags.

[0040] In a specific embodiment, the target tag usage frequency is obtained through the following steps:

[0041] M100, input each target text sample in the m target text sample sets obtained from the m second service platforms to the first service platform for labeling, and obtain the first label usage frequency list set F corresponding to the m target text sample sets 1 ={F 1 1, F 1 2, ..., F 1 i , ..., F 1 m}, F 1 i ={F 1 i1 , F 1 i2 , ..., F 1 ij , ..., F 1 in}, F 1 ij For B j The frequency of label usage in the i-th target text sample set can be understood as: the target text sample set corresponds to the second service platform one by one.

[0042] Specifically, the first service platform refers to the parent platform of the second service platform; it can be understood that: the label system in the second service platform is constructed with reference to the first service platform, and is expanded with other text labels according to its own region or service characteristics.

[0043] Specifically, F 1 ij Meet the following conditions:

[0044] F 1 ij =L 1 ij / L 1 i , where L 1 ij For B j The number of uses in the i-th target text sample set, L 1 i is the number of target text samples in the i-th target text sample set.

[0045] In a specific embodiment, the target text sample set is obtained through the following steps:

[0046] M001, obtaining an initial text list pre-stored in each second service platform; the initial text list includes a plurality of initial texts.

[0047] M002, according to the preset screening conditions, a number of initial texts are screened out from any initial text list and combined into a target text sample set; it can be understood as: according to any initial text list stored in the second service platform, the corresponding target text sample set is obtained after screening.

[0048] In a specific implementation, in step M002, the target text sample is determined by the following steps:

[0049] The text priorities corresponding to the initial texts are sorted from high to low, and the initial texts corresponding to the first n text priorities are determined as target text samples, where n is a preset text screening quantity threshold.

[0050] As described above, by calculating the text priorities of all initial texts in all initial text lists, a text screening quantity threshold number of initial texts can be screened out as target text samples according to the text priorities, so that the screened target text samples are at the forefront of priority, which is conducive to the analysis of text data.

[0051] In another specific embodiment, in step M002, the target text sample can also be determined by the following steps:

[0052] M0021, according to a preset text priority threshold, select a number of intermediate texts corresponding to the initial text list from each initial text list; the intermediate text refers to any initial text whose corresponding text priority is greater than the preset text priority threshold.

[0053] M0022, determine the number of target texts according to the number of intermediate texts corresponding to each initial text list.

[0054] Specifically, the target text quantity refers to a quantity that is not greater than the minimum intermediate text quantity, wherein the minimum intermediate text quantity refers to the minimum value of the m intermediate text quantities screened out from the m initial text lists; it can be understood that: each initial text list corresponds to an intermediate text quantity.

[0055] M0023, sorting the text priorities of several initial texts corresponding to any initial text list, and determining the intermediate texts corresponding to the text priorities of the first target text as the target text samples.

[0056] As described above, by determining the number of target texts and taking the number of target texts as the standard, the same number of initial texts can be screened out from each initial text list as target text samples, so that the same number of samples can be selected according to the differences in texts between different second service platforms to ensure the comprehensiveness of text analysis.

[0057] In another specific embodiment, the target text sample can also be obtained through the following steps:

[0058] E100, obtaining an initial text keyword set corresponding to each initial text in TE; the initial text list set includes a number of initial texts; the initial texts correspond to the initial text keyword set one by one; the initial text keyword set includes a number of initial text keywords.

[0059] In a specific embodiment, the step E100 includes the following steps:

[0060] E101, based on the keyword extraction model, extract several initial text keywords corresponding to the initial text itself from each initial text. Those skilled in the art are aware of any keyword extraction model in the prior art that can extract keywords from text, which will not be described in detail here.

[0061] E102, performing deduplication processing on the several initial text keywords, and combining the remaining initial text keywords into an initial text keyword set.

[0062] E200, removing a plurality of initial text keywords corresponding to each initial text from the corresponding initial text, and obtaining an initial text segment set corresponding to each initial text; the initial text segment set includes a plurality of initial text segments.

[0063] In a specific embodiment, step E200 also includes the following steps:

[0064] E201, removing a plurality of initial text keywords corresponding to each initial text from the corresponding initial text, and obtaining a plurality of pre-processed text segments corresponding to each initial text.

[0065] E202, according to a preset redundant word list, searching for a plurality of target redundant words from a plurality of preprocessed text segments corresponding to each initial text.

[0066] Specifically, the preset redundant word table refers to a data table recording a number of preset redundant words; for example, commonly used modal particles.

[0067] Specifically, the target redundant word refers to a word in the preprocessed text segment that is consistent with any preset redundant word.

[0068] E203, deleting the plurality of target redundant words from the plurality of preprocessed text segments, and obtaining an initial text segment corresponding to each preprocessed text segment.

[0069] As described above, after obtaining the initial text keywords, the preprocessed text fragments in the initial text are also obtained and converted into standard initial text fragments. Through the combination of the initial text keywords and the initial text fragments, the generated key text is more accurate and more in line with the original meaning of the initial text, and the standardized text is conducive to processing and analysis.

[0070] E300, input the initial text keyword set and initial text fragment set corresponding to each initial text into a preset language processing model to obtain the key text corresponding to each initial text; technical personnel in this field are aware of any language processing model in the prior art that can output corresponding text according to a number of words, and will not be repeated here.

[0071] In another specific embodiment, the key text can also be directly obtained through keywords, including the following steps:

[0072] L100, the CE in the keyword set CE of the text to be processed α Add to the preset keyword sequence and add CE α Remove from CE to obtain a new CE; the initial state of the preset keyword sequence is empty.

[0073] Specifically, the to-be-processed text keyword refers to any keyword collected by the target user based on the target person's verbal statement text information.

[0074] Specifically, the preset keyword sequence refers to a sequence for sequentially storing keywords of the text to be processed that are removed from the CE.

[0075] Specifically, CE={CE1, CE2, ..., CE α ,……,CE β}, where CE α is the αth text keyword to be processed, α=1, 2,…, β, β is the number of text keywords to be processed.

[0076] L200, traverse the text keywords to be processed in the new CE, calculate the correlation between each text keyword to be processed in the new CE and the CE α The co-occurrence probability of CE; that is, by constructing a co-occurrence matrix, find the co-occurrence probability of CE from the new CE. α The vector distance of the text to be processed is the closest and most reasonable. Those skilled in the art are aware of the specific implementation method of calculating the co-occurrence probability in the prior art, which will not be repeated here.

[0077] L300, according to each to-be-processed text keyword in the new CE and CE α The co-occurrence probability determines the maximum co-occurrence probability, and CE α Update to the to-be-processed text keyword corresponding to the maximum co-occurrence probability, return to execute L100, until the new CE is an empty set, and obtain CE α The corresponding maximum co-occurrence probabilities.

[0078] As described above, since a maximum co-occurrence probability is obtained in each cycle, when the new CE is an empty set, it has cycled β-1 times and obtained β-1 maximum co-occurrence probabilities.

[0079] L400, according to the maximum co-occurrence probabilities corresponding to each keyword of the text to be processed, obtain the first maximum co-occurrence probability list set P corresponding to CE = {P1, P2, ..., P α , ..., P β}, where P α ={P α1 , P α2 , ..., P αe , ..., P α(β-1)}, P αe For CE α The e-th maximum co-occurrence probability corresponding to the first keyword to be processed in the preset keyword sequence, e=1, 2, ..., β-1.

[0080] As mentioned above, by inputting β keywords of the text to be processed as starting keywords into the preset keyword sequence in turn, β maximum co-occurrence probability lists can be obtained, which is conducive to calculating the priority of each maximum co-occurrence probability list separately, so as to find the most reasonable order of sorting several keywords of the text to be processed and realize the order correction of keywords.

[0081] L500, according to P, obtain a first target co-occurrence probability list from β first maximum co-occurrence probability lists.

[0082] Specifically, in step L500, the first target co-occurrence probability list is obtained through the following steps:

[0083] L501, obtain CE α The co-occurrence probability list priority CS of the corresponding maximum co-occurrence probability list α ; can be understood as: CE α The corresponding maximum co-occurrence probability list refers to CE α The maximum co-occurrence probability list corresponding to when the starting keyword is added to the preset keyword sequence.

[0084] Among them, CS α Meet the following conditions:

[0085] CS α =∑ β-1 e=1 P αe .

[0086] L502, according to the co-occurrence probability list priority corresponding to each to-be-processed text keyword, determine the maximum co-occurrence probability list corresponding to the maximum co-occurrence probability list priority as the first target co-occurrence probability list.

[0087] As mentioned above, by adding up several co-occurrence probabilities in each maximum co-occurrence probability list respectively, the calculation result with the largest value can be found and the result can be used as the priority of the corresponding co-occurrence probability list. The larger the overall co-occurrence probability, the closer the average distance of several preset keywords is, and the most reasonable keyword sorting order can be found.

[0088] In another specific embodiment, when the computer program is executed by a processor, the following steps are implemented:

[0089] L01, if CE α When only one text keyword to be processed remains in the new CE after being removed from the CE, the remaining text keyword to be processed is added to the preset keyword sequence and the CE is obtained. α The corresponding β-2 maximum co-occurrence probabilities.

[0090] L02, according to the β-2 maximum co-occurrence probabilities corresponding to each keyword in the text to be processed, obtain the second maximum co-occurrence probability list set corresponding to CE; the second maximum co-occurrence probability list set includes β second maximum co-occurrence probability lists, and each second maximum co-occurrence probability list includes β-2 maximum co-occurrence probabilities.

[0091] L03, according to the second maximum co-occurrence probability list set, obtaining a second target co-occurrence probability list from the second maximum co-occurrence probability list set.

[0092] Specifically, the method for acquiring the second target co-occurrence probability list is consistent with the method for acquiring the first target co-occurrence probability list.

[0093] As mentioned above, when there is only one keyword left in the keyword set, there is no other keyword in the keyword set. Therefore, there is no need to calculate and compare to find the maximum co-occurrence probability. It can be directly added to the preset keyword sequence.

[0094] L600, input the to-be-processed text keywords in the preset keyword sequence corresponding to the first target co-occurrence probability list into the preset language processing model in the order in which they are added to the preset keyword sequence, so as to obtain the key text corresponding to CE.

[0095] In another specific embodiment, in step L600, the first target co-occurrence probability list is replaced with the second target co-occurrence probability list to obtain the key text corresponding to CE.

[0096] In the above, by obtaining a reasonable arrangement order of several keywords of the text to be processed, the order correction of several keywords of the text to be processed is realized, and several preset keywords are input into the preset language processing model in sequence according to the corrected order, and standardized key text is generated to realize the management and complete storage of the text.

[0097] In another specific embodiment, in step L600, the first target co-occurrence probability list is replaced by the second target co-occurrence probability list.

[0098] In another specific embodiment, the method further comprises the steps of:

[0099] L001, determining the initial text keyword set as a new text keyword set to be processed.

[0100] L002, according to the new to-be-processed text keyword set, obtain the key text corresponding to the initial text keyword set.

[0101] The above provides another form of obtaining standardized text, that is, when the data information collected by the target user is in text form rather than one-sided keywords, it is possible to extract several initial text keywords from the text and generate corresponding standardized text based on the several initial text keywords.

[0102] E400, obtaining a text weight corresponding to each initial text according to a plurality of initial texts, an initial text keyword set corresponding to each initial text, and a key text corresponding to each initial text.

[0103] Specifically, the text weight corresponding to the initial text meets the following conditions:

[0104] W 00 r =W 0 r / max(W 0 1,W 0 2,……,W 0 r ,……,W 0 g ), where W 00 r is the text weight corresponding to the rth initial text in the initial text list, W 0 r =δr1 ×N r ×δ r2 ×(δ r1 / δ rg ), δ r1 is the number of characters in the rth initial text, N r is the number of initial text keywords corresponding to the rth initial text, δ r2 is the number of characters of the key text corresponding to the rth initial text, the value range of r is 1 to g, and g is the number of initial texts in the initial text list set.

[0105] E500: When there is an initial text whose corresponding text weight is greater than a preset text weight threshold, the initial text whose corresponding text weight is greater than the preset text weight threshold is determined as the target text.

[0106] The above provides a technical solution for obtaining the corresponding key text based on the initial text keywords and the initial text fragments, and then calculating the text weight of the corresponding initial text based on the initial text and the key text. This calculation method makes full use of the structure and content information of the text to achieve a comprehensive evaluation of the text, and can quickly determine the effective and high-value target text, which is conducive to the processing and analysis of large amounts of text data.

[0107] In a specific embodiment, in step E500, the preset text weight threshold is obtained through the following steps:

[0108] E501, receiving a plurality of preprocessed texts and obtaining a text weight corresponding to each preprocessed text; the method for obtaining the text weight corresponding to the preprocessed text is consistent with the method for obtaining the text weight corresponding to the initial text.

[0109] E502, sorting the text weights corresponding to the plurality of preprocessed texts from small to large to obtain a weight sorting result.

[0110] E503: Determine the text weight at the target sorting position in the weight sorting result as a preset text weight threshold.

[0111] Specifically, the target ranking position refers to the position determined by technicians in this field in the weight ranking results according to actual needs; for example, it can be the middle position or the value at the 80% position.

[0112] In the above, by taking a number of preprocessed texts as sample sets and determining the text weight threshold according to the text weights of the preprocessed texts, the set text weight threshold is made more reasonable, which is conducive to selecting effective target texts.

[0113] M200, for F 1 iThe n first tag usage frequencies are sorted, and the target tag usage frequency corresponding to the i-th second service platform is determined according to a preset frequency determination condition.

[0114] Specifically, the preset frequency determination condition refers to: determining the value at the target position in the sorting result as the target tag usage frequency.

[0115] Specifically, the target position refers to a position determined by those skilled in the art in the art in the sorting result of the first tag usage frequency according to actual needs; for example, the target position may be the lower quartile in the sorting result of the first tag usage frequency.

[0116] S003, calculating the ratio of the third tag usage frequency corresponding to any second text tag to the second tag usage frequency corresponding to the second text tag itself.

[0117] S004: When the ratio is not less than a preset ratio threshold, a second text tag corresponding to a ratio not less than the preset ratio threshold is determined as a key text tag.

[0118] The specific steps are: when y / m≥θ, F 2 iu The corresponding second text label is determined as the key text label, where θ is a preset ratio threshold; for example, 80%.

[0119] As mentioned above, when the proportion of the number of second tag usage frequencies that are greater than the target tag usage frequency among the several second tag usage frequencies corresponding to any second text tag is not less than a preset ratio threshold, this second text tag is considered to be an important tag, and then this second text tag is added to the first text tag set to construct a more comprehensive and effective new tag system.

[0120] In a specific embodiment, a new label system can be constructed. Specifically, step M500 further includes the following steps:

[0121] M501, traverse B and several key text tags, when there is a key text tag that is the same as any first text tag in B, delete the key text tag that is the same as any first text tag in B.

[0122] M502, combines the remaining key text labels with B to construct a new label system.

[0123] In the above, by filtering out the key text tags that are the same as any first text tag from the filtered key text tags, there are no duplicate tags in the constructed new tag system, thus avoiding duplicate storage of tags.

[0124] S200 , labeling each initial text based on a plurality of preset key text labels, and obtaining a plurality of initial texts corresponding to each key text label.

[0125] The specific steps are: input each initial text in TE into m second service platforms for labeling, and obtain the second text list set B 2 ={B 2 1, B 2 2, ..., B 2 a , ..., B 2 c}, where B 2 a It refers to the ath second text list, and the ath second text list refers to a list composed of several initial texts corresponding to the ath key text tag; it can be understood that: the m second service platforms all receive g initial texts in the initial text list set.

[0126] S300 , obtaining a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0127] The specific steps are: according to the text priority of each initial text and B 2 , get the second text label score set TA corresponding to TA 0 ={TA 0 1.TA 0 2, ..., TA 0 a ,……,TA 0 c}, where TA 0 a For TA a The corresponding second text label score.

[0128] Specifically, the method for obtaining the second text label score is consistent with the method for obtaining the first text label score described below, and will not be repeated here.

[0129] S400: When there is a second text tag whose score is not less than the preset target tag score, a key text tag corresponding to the second text tag whose score is not less than the preset target tag score is determined as the target text tag.

[0130] Specifically, in step S400, the target tag score is obtained through the following steps:

[0131] S401, input each initial text into the first service platform for labeling, and obtain a plurality of initial texts corresponding to each first text label.

[0132] The specific steps are: input each initial text in TE into the first service platform for labeling, and obtain the first text list set B 1 ={B 1 1, B 1 2, ..., B 1 j , ..., B 1 n}, B 1 j ={B 1 j1 , B 1 j2 , ..., B 1 jd , ..., B 1 jh(j)}, where B 1 jd It refers to the dth initial text corresponding to the jth first text label, d=1, 2, ..., h(j), h(j) is the number of initial texts corresponding to the jth first text label; it can be understood that: the first text refers to any initial text marked with any first text label.

[0133] As mentioned above, by inputting each initial text into the first service platform for labeling, several first text labels corresponding to each initial text can be obtained, and therefore several initial texts corresponding to each first text label can also be obtained. By obtaining the number of initial texts corresponding to each first text label, it is helpful to measure the importance of each first text label.

[0134] S402: Acquire a first text tag score corresponding to each first text tag according to the text priority of each initial text and a number of initial texts corresponding to each first text tag.

[0135] The specific steps are: according to the text priority of each initial text and B 1 , get the first text label score set B corresponding to B 0 ={B 0 1, B 0 2, ..., B 0 j , ..., B 0 n}, where B 0 j For B j The corresponding first text label score.

[0136] Among them, B 0 j Meet the following conditions:

[0137] B0 j =(h(j) / g)×log(g / ∑ g r=1 (λ jr / W r )), where W r refers to the text priority of the rth initial text, λ jr The value of is 0 or 1. When the rth initial text tag has the jth first text tag, λ jr is 1, otherwise, λ jr is 0.

[0138] Among them, when ∑ g r=1 (λ jr / W r ) = 0, B 0 j =0.

[0139] As described above, by obtaining the number of initial texts corresponding to each first text label and the priority of each initial text, the score of each first text label can be calculated. The score can reflect the importance of the first text label, which is conducive to achieving a comprehensive evaluation of the importance of the label.

[0140] S403, sorting the scores of the first text labels from large to small, and determining the value at the target position in the sorting result as the target label score.

[0141] As described above, by inputting the initial text into the first service platform for labeling, and calculating several first text label scores according to the labeling result and the text priority of the initial text, the set target label score is made more reasonable, which is conducive to screening out more valuable target text labels.

[0142] In a specific embodiment, after step S400, the following steps are also included:

[0143] S500, traversing a plurality of first text tags and a plurality of determined target text tags, and when there is a target text tag that is the same as any of the first text tags, deleting the target text tag that is the same as any of the first text tags.

[0144] S600: Combine the remaining target text labels with a plurality of first text labels to construct a new label system.

[0145] As described above, by filtering out the target text tags that are the same as any first text tag from the filtered target text tags, there are no duplicate tags in the constructed new tag system, thus avoiding duplicate storage of tags.

[0146] As a parallel embodiment, the target text label can also be obtained by the following implementation method, including the following steps:

[0147] P100, from TE r Get TE r The corresponding keyword intermediate information set; the TE r The corresponding keyword intermediate information set includes TE r The number of initial text keywords in z r TE r The number of initial text keywords repeated in r 、z r The average number of characters of initial text keywords z 0 r and the number of characters in the intermediate text segment between each two adjacent initial text keywords.

[0148] For a better understanding, the number of repeated initial text keywords is given as an example: r When there are six initial text keywords in total, they are: initial text keyword 1, initial text keyword 1, initial text keyword 1, initial text keyword 2, initial text keyword 2, initial text keyword 3, among which initial text keyword 1 is repeated twice and initial text keyword 2 is repeated once, so the total number of repetitions is 2+1=3; it can also be understood as: for TE r The number of initial text keywords that need to be deleted when deduplicating the initial text keywords in the .

[0149] In a specific embodiment, in step P100, TE is obtained by the following steps: r The number of characters in the intermediate text fragment between each two adjacent initial text keywords:

[0150] P101, Get TE r The z initial text keywords in TE r The location information in.

[0151] P102, according to TE r The location information of each initial text keyword in TE r The intermediate text segment between each two adjacent initial text keywords in ; the intermediate text segment is also the preprocessed text segment obtained above.

[0152] P103, according to TE r The intermediate text segment between each two adjacent initial text keywords in the TE r The number of characters in the intermediate text segment between each two adjacent initial text keywords.

[0153] P200, according to TE r and TE r Corresponding keyword intermediate information set, obtain TE r The corresponding initial text score ψ r .

[0154] Among them, ψ r Meet the following conditions:

[0155] ψ r =(z r ×ρ r × 0 r ) / (z 00 r ×(ζ r +1)), where ρ r For TE r The corresponding target number of characters, z 00 r For TE r The average number of characters in the corresponding intermediate text segments.

[0156] In this embodiment, TE r The corresponding target number of characters refers to TE r The number of characters.

[0157] In another specific embodiment, ρ can also be determined by the following steps: r :

[0158] P201, obtain the number of characters of each initial text in TE.

[0159] P202, according to the number of characters of some initial text, TE r The number of characters is normalized to between 0 and λ, and the normalized result is determined as ρ r , where λ is the preset normalization threshold.

[0160] As mentioned above, when calculating the initial text score, taking into account the situation that other parameters are small and relatively close, while the number of characters in the initial text is large, the method of normalizing the number of characters in the initial text is adopted. This can effectively reduce the influence of the number of characters in the initial text on the calculated initial text score and improve the rationality of calculating the initial text score.

[0161] P300, based on TA, labels each initial text in TE and obtains the second text list set B 2 ={B 2 1, B 2 2, ..., B 2 a , ..., B 2c}, where B 2 a It refers to the ath second text list, and the ath second text list refers to a list composed of several initial texts corresponding to the ath key text tag.

[0162] P400, based on the initial text score and B corresponding to each initial text 2 , get the second text label score set ST corresponding to TA = {ST1, ST2, ..., ST a , ..., ST c}, where ST a For TA a The corresponding second text label score.

[0163] Specifically, in step P400, ST a Meet the following conditions:

[0164] ST a =(ξ(a) / g)×log(g / ∑ g r=1 (λ ar / ψ r ), where λ ar The value of is 0 or 1. When the rth initial text is marked with the ath key text label, λ ar is 1, otherwise, λ ar is 0, ξ(a) is B 2 a The number of initial texts in .

[0165] P500, when ST a When the score is not less than the preset tag score, the TA is obtained. a The target text label.

[0166] Specifically, in one embodiment, the preset tag score is a score set by a person skilled in the art according to actual needs or experience. In a parallel embodiment, by replacing the text priority in step S300 with the initial text score, that is, replacing W r Replace with ψ r The target label score obtained after calculating the first text label score is used as the preset target label score. The calculation process is the same and will not be repeated here.

[0167] As described above, by using the intermediate information set of keywords in the initial text, the relevant parameters of each keyword in the initial text are obtained, and the score of this initial text is calculated separately according to the number of characters in the initial text and the parameters of each keyword. What is different from the previous implementation method of obtaining the target text label is that the text priority in the previous implementation method is based on the overall information of several initial texts to measure the score of each initial text, while this embodiment adopts a method of calculating the score of each initial text separately, which simplifies the calculation process.

[0168] In a specific embodiment, each of the second service platforms is respectively provided with a preset label model for labeling text, and when the computer program is executed by the processor, the following steps are also implemented:

[0169] P001, receiving a plurality of first predicted text labels marked by a target user for any preset text in a preset text set.

[0170] Specifically, the preset text refers to any initial text in the initial text list set or any text among several initial texts randomly selected from the initial text list set.

[0171] P002, input any of the preset texts into several preset label models, and obtain several second predicted text labels marked by each preset label model for the any of the preset texts; it can be understood that: the any of the preset texts here refers to the preset text marked with several first preset text labels in step P001.

[0172] P003, according to the plurality of first predicted text labels and the plurality of second predicted text labels marked by each preset label model for any preset text, obtain the label overlap rate corresponding to each preset label model.

[0173] In a specific embodiment, in step P003, the label overlap rate corresponding to any preset label model is obtained through the following steps:

[0174] P0031, traverse a number of first predicted text labels, and obtain a third predicted text label from a number of second predicted text labels corresponding to any of the preset label models; the third predicted text label refers to a second predicted text label that is the same as any first predicted text label among the number of second predicted text labels corresponding to any of the preset label models.

[0175] Specifically, the plurality of second predicted text labels corresponding to the preset label model refer to the plurality of second predicted text labels marked by the preset label model for any preset text.

[0176] P0032, according to the number of the third predicted text labels and the number of the first predicted text labels, obtain the label overlap rate corresponding to any one of the preset label models.

[0177] Specifically, the label overlap rate corresponding to any of the preset label models meets the following conditions:

[0178] RE v =φ v / φ, where RE v It refers to the label overlap rate corresponding to the vth preset label model, φ v refers to the same number of the second predicted text labels as the first predicted text labels, and φ refers to the number of the first predicted text labels.

[0179] As mentioned above, for a preset text, each preset label model can obtain a corresponding label overlap rate. The higher the label overlap rate, the higher the label accuracy of the preset label model, which is conducive to obtaining the priority of several preset label models according to the preset text set.

[0180] In a preferred embodiment, the key text tag set TA can also be obtained through the following steps:

[0181] P1, according to the number of preset texts in the preset text set, obtain several label overlap rates corresponding to any preset label model; it can be understood that: any preset label model labels each preset text separately, and several label overlap rates corresponding to the preset label model will be obtained.

[0182] P2, normalize the label overlap rates corresponding to each preset label model to obtain the label model priority corresponding to each preset label model; a person skilled in the art is aware of any implementation method of normalization in the prior art, for example, a method of averaging the label overlap rates, which will not be described here.

[0183] P3, when g≥γ, the preset label model with the highest corresponding label model priority is determined as the target label model, and several text labels in the second service platform corresponding to the target label model are used as TA, where γ is a preset quantity threshold.

[0184] P4, when g<γ, a preset number of preset label models are screened out according to the label model priority, and text labels in a plurality of second service platforms corresponding to the preset number of preset label models are merged as TA.

[0185] As mentioned above, taking into account the amount of initial text, when the amount of initial text is small, the text tags in several second service platforms corresponding to the multiple preset tag models screened out are used as key text tags to increase the number of key text tags, making the tags for the initial text more comprehensive. When the amount of initial text is large, using a preset tag model can reduce the complexity of operations and improve tagging efficiency.

[0186] In another specific embodiment, the rationality of the acquired second predicted text label can also be verified, that is, after step P003, the following steps are also included:

[0187] P004, based on a number of second predicted text labels of each preset label model for any preset text tag, the label overlap rate corresponding to each preset label model and the preset model credibility of each preset label model, obtain the credibility priority of each second predicted text label; it can be understood that: the preset label model is an open source label model carrying credibility information, and the model credibility can also be tested through samples.

[0188] In a specific embodiment, in step P004, the credibility priority of any second predicted text label is obtained through the following steps:

[0189] P0041, according to the preset model credibility of each preset label model and a number of second predicted text labels corresponding to each preset label model, obtain the target model credibility of each preset label model.

[0190] Among them, the target model credibility of any preset label model meets the following conditions:

[0191] CR 0 v =CR v ×(QU1 / QU), where CR 0 v is the target model credibility of the vth preset label model, CR v is the preset model credibility of the vth preset label model, QU1 is the number of preset label models that mark any second predicted text label, and QU is the number of preset label models.

[0192] P0042, obtaining the credibility priority of any second predicted text label according to the label overlap rate corresponding to each preset label model and the target model credibility of each preset label model.

[0193] Specifically, the credibility priority of any second predicted text label meets the following conditions:

[0194] ω=∑ QUv=1 (RE v ×CR 0 v ).

[0195] As described above, the preset model credibility of each preset label model is adjusted by marking the number of preset label models of any second predicted text label and the total number of preset label models. That is, the fewer the number of preset label models that type out a second predicted text label, the less credible the typed second predicted text label is, which will reduce the credibility of the preset label model that types out the second predicted text label. Therefore, the credibility of the preset label model needs to be adjusted to make the calculated credibility priority more accurate.

[0196] P005, when there is a second predicted text label whose credibility priority is greater than a preset credibility priority threshold, the result of verifying that the second predicted text label whose corresponding credibility priority is greater than the preset credibility priority threshold meets the preset requirements; it can be understood that: verifying that the result of the second predicted text label meets the preset requirements means that the second predicted text label is a reasonable label.

[0197] In a specific embodiment, the credibility priority threshold is obtained through the following steps:

[0198] P0051, receiving a number of target processing texts and obtaining the credibility priority of each fourth predicted text label among a number of fourth predicted text labels corresponding to each target processing text; the method for obtaining the credibility priority of the fourth predicted text label is consistent with the method for obtaining the credibility priority of the second predicted text label.

[0199] Specifically, the target processing text refers to any initial text randomly selected from the initial text list set.

[0200] P0052, sorting the credibility priorities of a plurality of fourth predicted text labels from small to large to obtain a priority sorting result.

[0201] P0053, determining the credibility priority at the target ranking position in the priority ranking result as the credibility priority threshold.

[0202] As mentioned above, by taking several target processed texts as sample sets, several corresponding fourth predicted text labels and the credibility priority of each fourth predicted text label are obtained, and a credibility priority threshold is determined according to several credibility priorities, so that the set credibility priority threshold is more reasonable, which can improve the accuracy and rationality of label verification.

[0203] Embodiment 2

[0204] This embodiment provides a device for determining a target text label, the device comprising:

[0205] The first acquisition module is used to obtain the text priority of each initial text based on the content data information of each initial text in a number of pre-stored initial texts; the content data information of the initial text includes a number of keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in a number of initial texts and the creation time of the initial text itself.

[0206] The second acquisition module is used to label each initial text based on a plurality of preset key text labels, and acquire a plurality of initial texts corresponding to each key text label.

[0207] The third acquisition module is used to acquire the second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0208] The first determination module is configured to determine, when there is a second text tag score that is not less than a preset target tag score, a key text tag corresponding to the second text tag score that is not less than the preset target tag score as a target text tag.

[0209] The specific implementation method of the device during implementation is the same as that of Example 1, and will not be repeated here.

[0210] Embodiment 3

[0211] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0212] S100, obtaining the text priority of each initial text according to the content data information of each initial text among several pre-stored initial texts; the content data information of the initial text includes several keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in several initial texts and the creation time of the initial text itself.

[0213] S200 , labeling each initial text based on a plurality of preset key text labels, and obtaining a plurality of initial texts corresponding to each key text label.

[0214] S300 , obtaining a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0215] S400: When there is a second text tag whose score is not less than the preset target tag score, a key text tag corresponding to the second text tag whose score is not less than the preset target tag score is determined as the target text tag.

[0216] Embodiment 4

[0217] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0218] S100, obtaining the text priority of each initial text according to the content data information of each initial text among several pre-stored initial texts; the content data information of the initial text includes several keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in several initial texts and the creation time of the initial text itself.

[0219] S200 , labeling each initial text based on a plurality of preset key text labels, and obtaining a plurality of initial texts corresponding to each key text label.

[0220] S300 , obtaining a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag.

[0221] S400: When there is a second text tag whose score is not less than the preset target tag score, a key text tag corresponding to the second text tag whose score is not less than the preset target tag score is determined as the target text tag.

[0222] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for determining a target text label, characterized in that: The method comprises the following steps: S100, obtaining a text priority of each initial text according to content data information of each initial text among a plurality of pre-stored initial texts; the content data information of the initial text includes a plurality of keywords in the initial text itself, a total number of characters in the initial text itself, an average number of characters in the plurality of initial texts, and a creation time of the initial text itself; The text priority of the initial text meets the following conditions: W=ε1×(K1 / K2)+ε2×(K2 / K3)+ε3×(1 / K4), where W refers to the text priority of the corresponding initial text, K1 is the total number of characters of several keywords in the corresponding initial text, K2 is the total number of characters of the corresponding initial text, K3 is the average number of characters of several initial texts, K4 is the difference between the current time and the creation time of the corresponding initial text, ε1 is the preset first weight coefficient, ε2 is the preset second weight coefficient, and ε3 is the preset third weight coefficient; S200, labeling each initial text based on a plurality of preset key text labels, and obtaining a plurality of initial texts corresponding to each key text label; The method further provides a first service platform and a plurality of second service platforms, wherein the first service platform has a higher level than each second service platform, any two second service platforms have the same level, and the first service platform is in communication connection with each of the second service platforms; The key text labels are obtained by following the steps below: S001, input each initial text into a plurality of second service platforms for tagging, and obtain the second tag usage frequency of each second text tag in each second service platform; the second text tag is any text tag pre-configured in any second service platform; wherein the second tag usage frequency of the second text tag in the second service platform meets the following conditions: F 2 iux =L 2 iux / L 2 x , where F 2 iux It refers to the second label usage frequency of the u-th second text label in the i-th second service platform in the x-th second service platform, L 2 iux is the number of times the u-th second text label in the i-th second service platform is used in the x-th second service platform, L 2 x is the number of initial texts corresponding to the x-th second service platform; S002, according to a preset target tag usage frequency, respectively filter out a plurality of third tag usage frequencies corresponding to each second text tag from a plurality of second tag usage frequencies; the third tag usage frequency refers to any second tag usage frequency greater than the target tag usage frequency among a plurality of second tag usage frequencies corresponding to the second text tag; S003, calculating the ratio of the number of third tag usage frequencies corresponding to any second text tag to the number of second tag usage frequencies corresponding to the second text tag itself; S004, when the ratio is not less than a preset ratio threshold, determining the second text tag corresponding to the ratio not less than the preset ratio threshold as a key text tag; S300, obtaining a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag; S400: When there is a second text tag whose score is not less than the preset target tag score, a key text tag corresponding to the second text tag whose score is not less than the preset target tag score is determined as the target text tag.

2. The target text label determination method according to claim 1, characterized in that: The plurality of key text tags refers to the sum of key text tags obtained from a plurality of given second service platforms; The several initial texts refer to the sum of the initial texts obtained from several given second service platforms.

3. A target text label determination device, characterized in that: The device comprises: A first acquisition module is used to acquire the text priority of each initial text according to the content data information of each initial text in a plurality of pre-stored initial texts; the content data information of the initial text includes a plurality of keywords in the initial text itself, the total number of characters in the initial text itself, the average number of characters in the plurality of initial texts, and the creation time of the initial text itself; The text priority of the initial text meets the following conditions: W=ε1×(K1 / K2)+ε2×(K2 / K3)+ε3×(1 / K4), where W refers to the text priority of the corresponding initial text, K1 is the total number of characters of several keywords in the corresponding initial text, K2 is the total number of characters of the corresponding initial text, K3 is the average number of characters of several initial texts, K4 is the difference between the current time and the creation time of the corresponding initial text, ε1 is the preset first weight coefficient, ε2 is the preset second weight coefficient, and ε3 is the preset third weight coefficient; The second acquisition module is used to label each initial text based on a plurality of preset key text labels, and acquire a plurality of initial texts corresponding to each key text label; The device also includes a first service platform and a plurality of second service platforms, the first service platform has a higher level than each second service platform, any two second service platforms have the same level, and the first service platform is in communication connection with each second service platform; The device further includes a fourth acquisition module, and the fourth acquisition module includes: A fifth acquisition module is used to input each initial text into a plurality of second service platforms for tagging, and obtain the second tag usage frequency of each second text tag in each second service platform; the second text tag is any text tag pre-configured in any second service platform; wherein the second tag usage frequency of the second text tag in the second service platform meets the following conditions: F 2 iux =L 2 iux / L 2 x , where F 2 iux It refers to the second label usage frequency of the u-th second text label in the i-th second service platform in the x-th second service platform, L 2 iux is the number of times the u-th second text label in the i-th second service platform is used in the x-th second service platform, L 2 x is the number of initial texts corresponding to the x-th second service platform; A screening module, for screening out a plurality of third tag usage frequencies corresponding to each second text tag from a plurality of second tag usage frequencies according to a preset target tag usage frequency; the third tag usage frequency refers to any second tag usage frequency greater than the target tag usage frequency among the plurality of second tag usage frequencies corresponding to the second text tag; A calculation module, used for calculating the ratio of the usage frequency of the third tag corresponding to any second text tag to the usage frequency of the second tag corresponding to the second text tag itself; A second determination module is used to determine, when the ratio is not less than a preset ratio threshold, a second text tag corresponding to a ratio not less than the preset ratio threshold as a key text tag; A third acquisition module is used to acquire a second text tag score corresponding to each key text tag according to the text priority of each initial text and a number of initial texts corresponding to each key text tag; The first determination module is configured to determine, when there is a second text tag score that is not less than a preset target tag score, a key text tag corresponding to the second text tag score that is not less than the preset target tag score as a target text tag.

4. The target text label determination device according to claim 3, characterized in that: The plurality of key text tags refers to the sum of key text tags obtained from a plurality of given second service platforms; The several initial texts refer to the sum of the initial texts obtained from several given second service platforms.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the target text label determination method according to any one of claims 1 to 2 is implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the target text label determination method according to any one of claims 1 to 2 is implemented.

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