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    PublicationOpen Access
    Bridging the gap between pre-census and census-era historical data: devising a geo-sampling model to analyse agricultural production in the long run for Southeast Europe, 1840–1897
    (Edinburgh University Press, 2020) Gerrits, Piet; Department of History; Kabadayı, Mustafa Erdem; Boykov, Grigor; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A
    This research introduces a novel geo-spatial sampling model to overcome a major difficulty in historical economic geography of Bulgarian lands during a crucial period: immediately before and after the de facto independence of the territory from the Ottoman Empire in the second half of the nineteenth century. At its core it seeks to investigate the research question how the Bulgarian independence affected agricultural production in two regions (centered around the cities of Plovdiv and Ruse) of today's Bulgaria, for which there are conflicting yet empirically unsubstantiated claims concerning the economic impact of the political independence. Using our be-spoke geo-sampling strategy we believe, we have sampled regionally representative commensurable agricultural data from the 1840s Ottoman archival documentation, in accord with agricultural censuses conducted by the nascent nation state of Bulgaria in the 1890s.
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    PublicationOpen Access
    Overview of CLEF 2019 lab protestnews: extracting protests from news in a cross-context setting
    (Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yüret, Deniz; Yoltar, Çağrı; Gürel, Burak; Mutlu, Osman; Akdemir, Arda; Teaching Faculty; Faculty Member; Faculty Member; Researcher; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; 179996; N/A; 219277; N/A; N/A
    We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing. The lab consists of document, sentence, and token level information classification and extraction tasks that were referred as task 1, task 2, and task 3 respectively in the scope of this lab. The tasks required the participants to identify protest relevant information from English local news at one or more aforementioned levels in a cross-context setting, which is cross-country in the scope of this lab. The training and development data were collected from India and test data was collected from India and China. The lab attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted results and working notes respectively. We have observed neural networks yield the best results and the performance drops significantly for majority of the submissions in the cross-country setting, which is China.