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Item Cross-corpora analysis of spatial language: The case of fictive motion(Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2019-09-01) Egorova E; Aflaki N; Marchis Fagundes CK; Stock KThe way people describe where things are is one of the central questions of spatial information theory and has been the subject of considerable research. We investigate one particular type of location description, fictive motion (as in, The range runs along the coast). The use of this structure is known to highlight particular properties of the described entity, as well as to convey its configuration in physical space in an effective way. We annotated 496 fictive motion structures in seven corpora that represent different types of spatial discourse - news, travel blogs, texts describing outdoor pursuits and local history, as well as image and location descriptions. We analysed the results not only by examining the distribution of fictive motion structures across corpora, but also by exploring and comparing the semantic categories of verbs used in fictive motion. Our findings, first, add to our knowledge of location description strategies that go beyond prototypical locative phrases. They further reveal how the use of fictive motion varies across types of spatial discourse and reflects the nature of the described environment. Methodologically, we highlight the benefits of a cross-corpora analysis in the study of spatial language use across a variety of contexts.Item Challenges in creating an annotated set of geospatial natural language descriptions(Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2018-08-01) Aflaki N; Russell S; Stock K; Winter S; Griffin A; Sester MIn order to extract and map location information from natural language descriptions, a first step is to identify different language elements within the descriptions. In this paper, we describe a method and discuss the challenges faced in creating an annotated set of geospatial natural language descriptions using manual tagging, with the purpose of supporting validation and machine learning approaches to annotation and text interpretation.