GSAM: A deep neural network model for extracting computational representations of Chinese addresses fused with geospatial feature-Reference-Cited by-同舟云学术

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GSAM: A deep neural network model for extracting computational representations of Chinese addresses fused with geospatial feature

Published:2020-05 Issue: Volume:81 Page:101473
ISSN:0198-9715
Container-title:Computers, Environment and Urban Systems
language:en
Short-container-title:Computers, Environment and Urban Systems

Author:

Xu Liuchang,Du Zhenhong,Mao Ruichen,Zhang Feng,Liu Renyi

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Urban Studies,General Environmental Science,Ecological Modelling,Geography, Planning and Development

Reference43 articles.

1. k-means++: The advantages of careful seeding. Society for Industrial and;Arthur;Applied Mathematics,2007

2. A neural probabilistic language model;Bengio;Journal of Machine Learning Research,2003

3. Positional error in automated geocoding of residential addresses;Cayo;International Journal of Health Geographics,2003

4. You are where you tweet: A content-based approach to geo-locating twitter users;Cheng,2010

5. The language of place: Semantic value from geospatial context;Cocos,2017

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