Replication Data for: 'Lost in Space: Geolocation in Event Data'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders eve' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["event data","geocoding","NLP"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders eve' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders eve
event data; geocoding; NLP