Address similarity calculation method and device, equipment and storage medium
A similarity calculation and address technology, applied in computing, neural learning methods, instruments, etc., can solve the problems of low retrieval speed and accuracy of matching algorithms, low matching success rate of unregistered words, and complex address matching rules. The effect of improving first choice accuracy, improving retrieval speed and accuracy, and increasing success rate
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Embodiment 1
[0058] This embodiment provides a method for calculating address similarity, please refer to figure 1 , the method includes:
[0059] S1: Extract the text information of the input address, filter and segment the text information, and obtain multiple candidate words;
[0060] S2: Input multiple candidate words into an address vector generation model to obtain an initial address vector of the input address;
[0061] S3: Input the initial address vector into the address similarity calculation model based on the twin neural network, and combine the gradient descent algorithm of the ternary loss function to obtain the feature vector of the initial address vector;
[0062] S4: Calculate the cosine distance or L2 distance between the feature vector and the address vector in the standard address data set, to obtain a known address vector close to the input address.
[0063] When the amount of address information data is large, the problem of low matching success rate of unregistered...
Embodiment 2
[0134]The above-mentioned embodiment has described the address similarity calculation method in the embodiment of the present invention, and the address similarity calculation device in the embodiment of the present invention is described below, please refer to figure 2 , the address similarity calculation device in the embodiment of the present invention includes:
[0135] The input address preprocessing module 1 is used to extract the text information of the input address, filter and segment the text information, and obtain a plurality of candidate words;
[0136] The initial vector forming module 2 is used to input a plurality of candidate words into the address vector generation model to obtain the initial address vector of the input address;
[0137] The feature vector extraction module 3 is used to input the initial address vector into the depth adaptation network model based on the ternary loss to obtain the feature vector of the initial address vector;
[0138] The s...
Embodiment 3
[0148] The second embodiment described above describes in detail the address similarity calculation device in the embodiment of the present invention from the perspective of modular functional entities, and the following describes the address similarity calculation device in the embodiment of the present invention in detail from the perspective of hardware processing.
[0149] Please see image 3 , the address similarity calculation device 500 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (for example, one or more processors) and memory 520 , one or more storage media 530 (such as one or more mass storage devices) for storing application programs 533 or data 532 . Wherein, the memory 520 and the storage medium 530 may be temporary storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure)...
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