Abstract
Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.
Highlights
Geographical addresses are the most important basic data resources in the construction of a smart city
This paper proposes a contrast learning address matching algorithm
We propose to use the attention mechanism to represent the semantic information of the address, so that the semantic vector can express richer semantic information by assigning different weights
Summary
Geographical addresses are the most important basic data resources in the construction of a smart city. The literal similarity between the two geographical addresses was calculated from a certain measurement dimension and the threshold was manually set [1]. Jaccard [5] brought up a new way which obtains a more accurate effect on short address by calculating the local similarity of two addresses, but it does not work well for long addresses. Afterwards, the N-gram approach based on vector space was proposed [6], which converts addresses to vector representations in the same vector space, and calculates the similarity using mathematical methods for example cosine similarity [7]. All the traditional methods mentioned above are still inadequate
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
