@inproceedings{blessing-schutze-2010-fine,
    title = "Fine-Grained Geographical Relation Extraction from {W}ikipedia",
    author = {Blessing, Andre  and
      Sch{\"u}tze, Hinrich},
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Piperidis, Stelios  and
      Rosner, Mike  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}`10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/L10-1358/",
    abstract = "In this paper, we present work on enhancing the basic data resource of a context-aware system. Electronic text offers a wealth of information about geospatial data and can be used to improve the completeness and accuracy of geospatial resources (e.g., gazetteers). First, we introduce a supervised approach to extracting geographical relations on a fine-grained level. Second, we present a novel way of using Wikipedia as a corpus based on self-annotation. A self-annotation is an automatically created high-quality annotation that can be used for training and evaluation. Wikipedia contains two types of different context: (i) unstructured text and (ii) structured data: templates (e.g., infoboxes about cities), lists and tables. We use the structured data to annotate the unstructured text. Finally, the extracted fine-grained relations are used to complete gazetteer data. The precision and recall scores of more than 97 percent confirm that a statistical IE pipeline can be used to improve the data quality of community-based resources."
}