Patent ID: 11941522
Assignee: ZHEJIANG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. The address information feature extraction method based on a deep neural network model according to claim 1, wherein a specific implementation process of the step S5 comprises steps of:
S51: obtaining output SAN, SAN-1, SAN-2, SAN-3 of the address text from the last four layers of the self-transformer sub-modules in the feature extraction module, and performing average pooling and max pooling on SAN, SAN-1, SAN-2, SAN-3, respectively, then adding all pooling results, to obtain final semantic feature expression sentEmbed0 of the address text;
S52: calculating a Euclidean distance range sent_range of all the address texts in a semantic feature vector space and a Euclidean distance range coor_range in a geospatial feature vector space, respectively;
performing a dimension-removing operation on the semantic feature vector sentEmbed0 and the geospatial feature vector coorEmbed0 of each of the address texts, and weighting, through setting a weight value λ, the dimension-removed feature vectors, to obtain the processed semantic feature vector sentEmbed and geospatial feature vector coorEmbed:, sentEmbed
   =
   
    sentEmbed
    *
    
     sent_range
     coor_range
    
    *
    λ
   
  
  ⁢
  

  
   coorEmbed
   =
   
    coorEmbed
    *
    
     (
     
      1
      -
      /
     
     )
    
   
  
 

directly splicing the processed two kinds of feature vectors, to form a fusion feature vector:

concatEmbed={sentEmbed,coorEmbed};and

S53: clustering all the fusion feature vectors through the K-Means clustering algorithm in combination with Elkan distance calculation optimization algorithm, Mini-Batch K-means strategy and K-means++ clustering center initialization scheme, so as to obtain a semantic-geospatial fusion clustering result.