  Recognizing toponyms and resolving them to their real-world referents is
required for providing advanced semantic access to textual data. This process
is often hindered by the high degree of variation in toponyms. Candidate
selection is the task of identifying the potential entities that can be
referred to by a toponym previously recognized. While it has traditionally
received little attention in the research community, it has been shown that
candidate selection has a significant impact on downstream tasks (i.e. entity
resolution), especially in noisy or non-standard text. In this paper, we
introduce a flexible deep learning method for candidate selection through
toponym matching, using state-of-the-art neural network architectures. We
perform an intrinsic toponym matching evaluation based on several new realistic
datasets, which cover various challenging scenarios (cross-lingual and regional
variations, as well as OCR errors). We report its performance on candidate
selection in the context of the downstream task of toponym resolution, both on
existing datasets and on a new manually-annotated resource of
nineteenth-century English OCR'd text.
