Odjel za računarstvo Hrvatske sekcije IEEE u suradnji s TakeLab-om te Znanstvenim centrom izvrsnosti za znanost o podatcima i kooperativne sustave poziva Vas na predavanje
"Topic-based and Cross-lingual Scaling of Political Text"
koje će održati Simone Paolo Ponzetto, sa Sveučilišta u Mannheimu, Njemačka, u četvrtak 26. listopada 2017. u 15 sati u Sivoj vijećnici Fakulteta elektrotehnike i računarstva Sveučilišta u Zagrebu, Unska 3.
Predavanje je na engleskom jeziku, predviđeno trajanje s raspravom je 60 minuta, otvoreno je za sve zainteresirane te se posebno pozivaju studenti.
Više o predavanju i predavaču pročitajte u opširnijem sadržaju obavijesti.
Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models, such as Wordscores and Wordfish, scale texts based on relative word usage; by doing so, they do not take into consideration topical information and cannot be used for cross-lingual analyses. In our talk, we present our efforts toward developing a topic-based and cross-lingual political text scaling approach.
First we introduce our initial work, TopFish, a multilevel computational method that integrates topic detection and political scaling and shows its applicability for temporal aspect analyses of political campaigns (pre-primary elections, primary elections, and general elections). Next, we present a new text scaling approach that leverages semantic representations of text and is suitable for cross-lingual political text scaling. We also propose a simple and straightforward setting for quantitative evaluation of political text scaling.
Simone Paolo Ponzetto is Professor of Information Systems at the University of Mannheim and member of the Data and Web Science Group, where he leads the Natural Language Processing and Information Retrieval group. His main research interests lie in the areas of knowledge acquisition, text understanding, and the application of natural language processing methods for research in the humanities and social sciences.