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Geoparsing is an important task in geographic information retrieval. Ageoparsing system, known as a geoparser, takes some texts as the input andoutputs the recognized place mentions and their location coordinates. In June2019, a geoparsing competition, Toponym Resolution in Scientific Papers, washeld as one of the SemEval 2019 tasks. The winning teams developed neuralnetwork based geoparsers that achieved outstanding performances (over 90%precision, recall, and F1 score for toponym recognition). This exciting resultbrings the question "are we there yet?", namely have we achieved high enoughperformances to possibly consider the problem of geoparsing as solved? Onelimitation of this competition is that the developed geoparsers were tested ononly one dataset which has 45 research articles collected from the particulardomain of Bio-medicine. It is known that the same geoparser can have verydifferent performances on different datasets. Thus, this work performs asystematic evaluation of these state-of-the-art geoparsers using our recentlydeveloped benchmarking platform EUPEG that has eight annotated datasets, ninebaseline geoparsers, and eight performance metrics. The evaluation resultsuggests that these new geoparsers indeed improve the performances ofgeoparsing on multiple datasets although some challenges remain.

Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform