Abstract
Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.- Anthology ID:
- D17-2015
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Lucia Specia, Matt Post, Michael Paul
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–90
- Language:
- URL:
- https://aclanthology.org/D17-2015
- DOI:
- 10.18653/v1/D17-2015
- Cite (ACL):
- Ludwig Richter, Johanna Geiß, Andreas Spitz, and Michael Gertz. 2017. HeidelPlace: An Extensible Framework for Geoparsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 85–90, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- HeidelPlace: An Extensible Framework for Geoparsing (Richter et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/D17-2015.pdf