Abstract
Nowadays, Volunteered Geographic Information (VGI) is commonly used in research and practical applications. However, the quality assurance of such a geographic data remains a problem. In this study we use machine learning and natural language processing to improve record retrieval by category (e.g. restaurant, museum, etc.) from Wikimapia Points of Interest data.We use textual information contained in VGI records to evaluate its ability to determine the category label. The performance of the trained classifier is evaluated on the complete dataset and then is compared with its performance on regional subsets. Preliminary analysis shows significant difference in the classifier performance across the regions. Such geographic differences will have a significant effect on data enrichment efforts such as labeling entities with missing categories.
Original language | English |
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Title of host publication | Proceedings of the 12th Workshop on Geographic Information Retrieval, GIR 2018 |
Editors | Christopher B. Jones, Ross S. Purves |
Publisher | Association for Computing Machinery, Inc |
ISBN (Electronic) | 9781450360340 |
DOIs | |
State | Published - Nov 6 2018 |
Event | 12th Workshop on Geographic Information Retrieval, GIR 2018 - Seattle, United States Duration: Nov 6 2018 → … |
Publication series
Name | Proceedings of the 12th Workshop on Geographic Information Retrieval, GIR 2018 |
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Conference
Conference | 12th Workshop on Geographic Information Retrieval, GIR 2018 |
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Country/Territory | United States |
City | Seattle |
Period | 11/6/18 → … |
Funding
∗Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http: //energy.gov/downloads/doe-public-access-plan).
Keywords
- Crowd-sourcing
- Machine learning
- Natural language processing