GSP (Geo-Semantic-Parsing): Geoparsing and Geotagging with Machine Learning on Top of Linked Data

  • Marco Avvenuti
  • Stefano CresciEmail author
  • Leonardo Nizzoli
  • Maurizio Tesconi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)


Recently, user-generated content in social media opened up new alluring possibilities for understanding the geospatial aspects of many real-world phenomena. Yet, the vast majority of such content lacks explicit, structured geographic information. Here, we describe the design and implementation of a novel approach for associating geographic information to text documents. GSP exploits powerful machine learning algorithms on top of the rich, interconnected Linked Data in order to overcome limitations of previous state-of-the-art approaches. In detail, our technique performs semantic annotation to identify relevant tokens in the input document, traverses a sub-graph of Linked Data for extracting possible geographic information related to the identified tokens and optimizes its results by means of a Support Vector Machine classifier. We compare our results with those of 4 state-of-the-art techniques and baselines on ground-truth data from 2 evaluation datasets. Our GSP technique achieves excellent performances, with the best \(F1 = 0.91\), sensibly outperforming benchmark techniques that achieve \(F1 \le 0.78\).


Geoparsing Machine learning Linked data Twitter 



This research is supported in part by the EU H2020 Program under the schemes INFRAIA-1-2014-2015: Research Infrastructures grant agreement #654024 SoBigData: Social Mining & Big Data Ecosystem.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PisaPisaItaly
  2. 2.Institute for Informatics and TelematicsIIT-CNRPisaItaly

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