Erdem Yörük


2020

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Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020
Ali Hürriyetoğlu | Erdem Yörük | Vanni Zavarella | Hristo Tanev
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

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Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report
Ali Hürriyetoğlu | Vanni Zavarella | Hristo Tanev | Erdem Yörük | Ali Safaya | Osman Mutlu
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences.

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COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules
Ali Hürriyetoğlu | Ali Safaya | Osman Mutlu | Nelleke Oostdijk | Erdem Yörük
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting. However, on the test data the performance of the integration was slightly lower than our best performing deep learning model. These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems. We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.

2016

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Towards Building a Political Protest Database to Explain Changes in the Welfare State
Çağıl Sönmez | Arzucan Özgür | Erdem Yörük
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities