@inproceedings{vogel-etal-2012-atlis,
    title = "{ATLIS}: Identifying Locational Information in Text Automatically",
    author = "Vogel, John  and
      Verhagen, Marc  and
      Pustejovsky, James",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Do{\u{g}}an, Mehmet U{\u{g}}ur  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}`12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/L12-1610/",
    pages = "612--616",
    abstract = "ATLIS (short for “ ATLIS Tags Locations in Strings”) is a tool being developed using a maximum-entropy machine learning model for automatically identifying information relating to spatial and locational information in natural language text. It is being developed in parallel with the ISO-Space standard for annotation of spatial information (Pustejovsky, Moszkowicz {\&} Verhagen 2011). The goal of ATLIS is to be able to take in a document as raw text and mark it up with ISO-Space annotation data, so that another program could use the information in a standardized format to reason about the semantics of the spatial information in the document. The tool (as well as ISO-Space itself) is still in the early stages of development. At present it implements a subset of the proposed ISO-Space annotation standard: it identifies expressions that refer to specific places, as well as identifying prepositional constructions that indicate a spatial relationship between two objects. In this paper, the structure of the ATLIS tool is presented, along with preliminary evaluations of its performance."
}