A Geo-Parser for German Documents with Environmental Context

Nicolas Doms (), Thorsten Schlachter () and Lisa Hahn-Woernle ()
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Nicolas Doms: Karlsruhe Institute of Technology (KIT)
Thorsten Schlachter: Karlsruhe Institute of Technology (KIT)
Lisa Hahn-Woernle: Baden-Württemberg State Institute for the Environment, Survey and Nature Conservation

A chapter in Advances and New Trends in Environmental Informatics, 2025, pp 21-33 from Springer

Abstract: Abstract Environmental information is often bound to some geographic entity, be it continent, country, city, or a smaller entity like a forest or a water body. Consequently, documents with environmental context also contain geographic entities. A geo-parser can help to understand what geographic information is present in the document. This information can then be used to display the geographic entities on a map, to group related data as a facet in an internet search, or to enable links between these documents to other documents that refer to the same geographic entity. There have been numerous geo-parsers in the past, however, none of them dealt explicitly with German documents with environmental context. This scenario features a number of challenges that will be explained before a solution is proposed in this publication. As the geo-parser requires some sort of a reference dataset with geographic names and geographic areas, different datasets are analyzed before the most fitting one is picked for the implementation. Furthermore, an evaluation dataset containing pre-tagged geographical entities is created and presented briefly, before the proposed solution is evaluated against the very same dataset.

Keywords: Geo-Parsing; Machine Learning; Natural Language Processing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-85284-8_2

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