  Geoparsing is an important task in geographic information retrieval. A
geoparsing system, known as a geoparser, takes some texts as the input and
outputs the recognized place mentions and their location coordinates. In June
2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was
held as one of the SemEval 2019 tasks. The winning teams developed neural
network based geoparsers that achieved outstanding performances (over 90%
precision, recall, and F1 score for toponym recognition). This exciting result
brings the question "are we there yet?", namely have we achieved high enough
performances to possibly consider the problem of geoparsing as solved? One
limitation of this competition is that the developed geoparsers were tested on
only one dataset which has 45 research articles collected from the particular
domain of Bio-medicine. It is known that the same geoparser can have very
different performances on different datasets. Thus, this work performs a
systematic evaluation of these state-of-the-art geoparsers using our recently
developed benchmarking platform EUPEG that has eight annotated datasets, nine
baseline geoparsers, and eight performance metrics. The evaluation result
suggests that these new geoparsers indeed improve the performances of
geoparsing on multiple datasets although some challenges remain.
