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

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 in- formation 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 extract- ing 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 tech- nique achieves excellent performances, with the best F 1 = 0.91, sensibly outperforming benchmark techniques that achieve F 1 ≤ 0.78.