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Recognition Method of New Address Elements in Chinese Address Matching Based on Deep Learning

Authors :
Hongwei Zhang
Fu Ren
Huiting Li
Renfei Yang
Shuai Zhang
Qingyun Du
Source :
ISPRS International Journal of Geo-Information, Vol 9, Iss 12, p 745 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Location services based on address matching play an important role in people’s daily lives. However, with the rapid development of cities, new addresses are constantly emerging. Due to the untimely updating of word segmentation dictionaries and address databases, the accuracy of address segmentation and the certainty of address matching face severe challenges. Therefore, a new address element recognition method for address matching is proposed. The method first uses the bidirectional encoder representations from transformers (BERT) model to learn the contextual information and address model features. Second, the conditional random field (CRF) is used to model the constraint relationships among the tags. Finally, a new address element is recognized according to the tag, and the new address element is put into the word segmentation dictionary. The spatial information is assigned to it, and it is put into the address database. Different sequence tagging models and different vector representations of addresses are used for comparative evaluation. The experimental results show that the method introduced in this paper achieves the maximum generalization ability, its F1 score is 0.78, and the F1 score on the testing dataset also achieves a high value (0.95).

Details

Language :
English
ISSN :
22209964
Volume :
9
Issue :
12
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.74e8c1397480d83b1fd033daa51f7
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi9120745