Centre for Social Sciences Political and Legal Text Mining and Artificial Intelligence Laboratory (poltextLAB)

Miklós SEBŐK is a Senior Research Fellow of the Centre of Social Sciences, Hungarian Academy of Sciences (CSS HAS) in Budapest. He earned an M.A. degree in politics at the University of Virginia and an M.A. degree in economics at the Corvinus University of Budapest. He received his Ph.D. in Political Science from ELTE University of Budapest. He currently serves as the director of thet Institute for Political Science at Centre for Social Sciences and the research director of the Hungarian Comparative Agendas Project and the research co-director of the Artificial Intelligence National Lab at CSS, Budapest. His research interests include political economy and public policy and the application of text mining and machine learning methods in these fields.

Orsolya RING received her Ph.D. in History from ELTE University of Budapest. She is working in the poltextLAB project on creation and classification of large-scale newspaper corpora and elaboration of a domain-specific method for Hungarian sentiment analysis applying various machine learning methods. She is also working on the building of large-scale historical text corpora and its analysis by NLP methods in the Research Group Computational Social Science (CSS-RECENS).

Ákos MÁTÉ studied political economy (PhD, Central European University) and network science (Advanced Certificate, Central European University). His main research interest is the application of  quantitative text analysis and other big data methods in the political economy field. In the poltextLAB project he is taking part relating to cloud infrastructure and processing large-scale text corpora.

György Márk KIS studied political science (BA, ELTE), public policy (MA, Central European University), and statistics (MSc, ELTE). During the past few years he worked at NGOs, in market research, and also served as an external contributor to the Hungarian CAP project. His primary fields are network science, the statistical modelling of complex systems and their application to the study of policy dynamics.

Csaba MOLNÁR studied political science (BA, MA Corvinus University of Budapest, BA, Nottingham Trent University). He is currently a PhD student of the Doctoral School of Political Science of the Corvinus University of Budapest. In the poltextLAB project, he is responsible for  NLP-related and database building tasks. His main research fields are right-wing radicalism and legislative studies. He also participates in the Hungarian Comparative Agendas Project where he works on legislative database development.

Rebeka KISS earned her bachelor's degree in public administration, then an undivided master's degree in science of public governance (University of Public Service). She is currently a PhD student of the Doctoral School of Public Administration Sciences of the University of Public Service, also a law student at the Faculty of Law of Eötvös Loránd University (ELTE). Her main field of research is legislation. Within constitutional law, her research focuses on the explanatory memorandum of laws. She has been involved in the CAP and poltextLAB projects since 2019.

István ÜVEGES is a researcher at the HUN-REN Centre for Social Sciences, Political and Legal Text Mining and Artificial Intelligence Laboratory (poltextLAB), and a Computational Linguist at MONTANA Knowledge Management Ltd. He previously earned BSc degree in Computer Science, and MA degree in Theoretical Linguistics. His main interests include practical applications of Automation, Artificial Intelligence (Machine Learning), Computational Propaganda, Legal Language (legalese) studies and the Plain Language Movement.

Ákos HOLÁNYI is a doctoral student of international and European studies at the Doctoral School of Public Administration Sciences of the University of Public Service, Hungary. He previously earned an undergraduate degree in Human Geography at Durham University, UK and a postgraduate degree in Balkan, Eurasian and Central European Studies at Charles University, Czechia. His current research explores the interest advocacy of small states in the European Union as well as the regional cooperation platforms of Central European states.

Viktor KOVÁCS is a 4th year Computer Science Engineer student at University of Szeged. His primary interests include machine learning, neural networks and natural language processing. He currently writes his bachelor's thesis on diagnostic classification of schizophrenia using state-of-the-art deep learning models.