{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lost-in-space-geolocation-in-event-data","title":"Lost in Space: Geolocation in Event Data","arxiv_id":"1611.04837","date":"2016-11-14","proceeding":null,"authors":["Sophie J. Lee","Howard Liu","Michael D. Ward"],"abstract":"Extracting the \"correct\" location information from text data, i.e.,\ndetermining the place of event, has long been a goal for automated text\nprocessing. To approximate human-like coding schema, we introduce a supervised\nmachine learning algorithm that classifies each location word to be either\ncorrect or incorrect. We use news articles collected from around the world\n(Integrated Crisis Early Warning System [ICEWS] data and Open Event Data\nAlliance [OEDA] data) to test our algorithm that consists of two stages. In the\nfeature selection stage, we extract contextual information from texts, namely,\nthe N-gram patterns for location words, the frequency of mention, and the\ncontext of the sentences containing location words. In the classification\nstage, we use three classifiers to estimate the model parameters in the\ntraining set and then to predict whether a location word in the test set news\narticles is the place of the event. The validation results show that our\nalgorithm improves the accuracy rate of the current geolocation methods of\ndictionary approach by as much as 25%.","url_abs":"http://arxiv.org/abs/1611.04837v1","url_pdf":"http://arxiv.org/pdf/1611.04837v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lost-in-space-geolocation-in-event-data","repo_url":"https://github.com/haoliuhoward/LostinSpace-PSRM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}