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A Method of Address Information Feature Extraction Based on Deep Neural Network Model

A deep neural network and address information technology, which is applied in the field of address information feature extraction based on a deep neural network model, can solve the problems of inability to dig deep into the connotation of text features, weak generalization, and information islands, and achieve model structure and training framework The effects of perfection, efficient extraction, accurate fitting and efficient calculation

Active Publication Date: 2021-09-17
ZHEJIANG UNIV
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Problems solved by technology

However, the current theoretical research centered on unstructured text management or address coding is unable to dig deep into the characteristic connotation of text, leading to prominent problems such as information islands, additional data dependence, and weak generalization in task processing, which limits Use of address data in the field of smart cities

Method used

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  • A Method of Address Information Feature Extraction Based on Deep Neural Network Model
  • A Method of Address Information Feature Extraction Based on Deep Neural Network Model
  • A Method of Address Information Feature Extraction Based on Deep Neural Network Model

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Embodiment 1

[0250] In this embodiment, an address text data set is constructed with 2 million pieces of place name and address data in Shangcheng District, Hangzhou City, and feature vector extraction is performed on it. The basic steps are as described in the aforementioned S1-S7, and will not be described in detail. The following mainly demonstrates some specific implementation details and effects of each step.

[0251] 1. According to the method described in steps S1-S7, use the TensorFlow deep learning framework to build ALM and GSAM, and set the save point of the model at the same time, save the neural network parameter variables except the target task module, so as to facilitate the transplantation in the next fine-tuning task; The hyperparameters of the model are set through the hype-para.config configuration file, and the specific contents mainly include the following categories:

[0252] 1) Training sample size batch_size: 64; 2) Initial learning rate η: 0.00005; 3) Number of tra...

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Abstract

The invention discloses a method for extracting address information features based on a deep neural network model. The invention utilizes a deep neural network architecture to transform tasks such as text feature extraction, address normalization construction, and semantic space fusion into quantifiable deep neural network model construction and training optimization problems. Taking the characters in the address as the basic input unit, a language model is designed to express it in a vectorized manner, and then the key technology of the standardized construction of place names and addresses is realized through the neural network target task. At the same time, considering the expression characteristics of place name address space, a feature fusion scheme of address semantic-space is proposed, a weighted clustering method and a feature fusion model are designed, and fusion vectors with semantic features and spatial features are extracted from natural language address texts. The invention can realize the extraction of characteristic content of address information, its structure has high expandability, can unify the solution ideas of address information tasks, and has great significance for urban construction.

Description

technical field [0001] The invention relates to the field of address information mining of GIS (Geographic Information System), in particular to a method for extracting features of address information based on a deep neural network model. Background technique [0002] With the continuous improvement of GIS cognition and application capabilities, address information has gradually become the core resource in the era of smart cities. The semantic and spatial connotations carried in its content are the basic support for the construction of geographic ontology and spatiotemporal semantic framework in smart cities. It is of great theoretical and practical significance for the integration and understanding of urban semantics and spatial content to allow computers to deeply refine the comprehensive characteristics of place names and addresses from the perspective of understanding address texts and form quantitative expressions in numerical form. However, the current theoretical rese...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/29G06F16/35G06F40/30G06F40/126G06K9/62
CPCG06F16/29G06F16/35G06F40/126G06F40/30G06F18/23213G06N3/084G06N3/048G06N3/045G06N3/08G06N7/01
Inventor 张丰毛瑞琛杜震洪徐流畅叶华鑫
Owner ZHEJIANG UNIV
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