Abstract
A wealth of unstructured textual content contains valuable geographic insights. This geographic information holds significance across diverse domains, including geographic information retrieval, disaster management, and spatial humanities. Despite significant progress in the extraction of geographic information from texts, numerous unresolved challenges persist, ranging from methodologies, systems, data, and applications to privacy concerns. This workshop will serve as a platform for the discourse of recent breakthroughs, novel ideas, and conceptual innovations in this field.
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References
Allen, T., et al.: Global hotspots and correlates of emerging zoonotic diseases. Nat. Commun. 8(1), 1–10 (2017)
Arulanandam, R., Savarimuthu, B.T.R., Purvis, M.A.: Extracting crime information from online newspaper articles. In: Proceedings of the Second Australasian Web Conference, vol. 155, pp. 31–38 (2014)
Gritta, M., Pilehvar, M.T., Limsopatham, N., Collier, N.: What’s missing in geographical parsing? Lang. Resour. Eval. 52(2), 603–623 (2018)
Haris, E., Gan, K.H.: Mining graphs from travel blogs: a review in the context of tour planning. Inform. Technol. Tourism 17(4), 429–453 (2017)
Hu, X., Sun, Y., Kersten, J., Zhou, Z., Klan, F., Fan, H.: How can voting mechanisms improve the robustness and generalizability of toponym disambiguation? Int. J. Appl. Earth Obs. Geoinf. 117, 103191 (2023)
Hu, X., et al.: Location reference recognition from texts: A survey and comparison. arXiv preprint arXiv:2207.01683 (2022)
Hu, Y., Adams, B.: Harvesting big geospatial data from natural language texts. In: Werner, M., Chiang, Y.-Y. (eds.) Handbook of Big Geospatial Data, pp. 487–507. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55462-0_19
Hu, Y., et al.: Geo-knowledge-guided gpt models improve the extraction of location descriptions from disaster-related social media messages. Int. J. Geogr. Inf. Sci. 37(11), 2289–2318 (2023)
Kinsella, S., Murdock, V., O’Hare, N.: " i’m eating a sandwich in glasgow" modeling locations with tweets. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents, pp. 61–68 (2011)
Melo, F., Martins, B.: Automated geocoding of textual documents: a survey of current approaches. Trans. GIS 21(1), 3–38 (2017)
Milusheva, S., Marty, R., Bedoya, G., Williams, S., Resor, E., Legovini, A.: Applying machine learning and geolocation techniques to social media data (twitter) to develop a resource for urban planning. PLOS ONE 16(2), 1–12 (02 2021). https://doi.org/10.1371/journal.pone.0244317
Purves, R.S., Clough, P., Jones, C.B., Hall, M.H., Murdock, V.: Geographic information retrieval: Progress and challenges in spatial search of text. Foundations and Trends® in Information Retrieval 12(2-3), 164–318 (2018). https://doi.org/10.1561/1500000034
Scalia, G., Francalanci, C., Pernici, B.: Cime: context-aware geolocation of emergency-related posts. Geoinformatica 26(1), 125-157 (2022). https://doi.org/10.1007/s10707-021-00446-x
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Hu, X., Purves, R., Moncla, L., Kersten, J., Stock, K. (2024). 2nd International Workshop on Geographic Information Extraction from Texts (GeoExT 2024). In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_60
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