The SUMMA Platform Prototype

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams

"Just the Facts" with PALOMAR: Detecting Protest Events in Media Outlets and Twitter

The volume and velocity of available online sources have changed journalistic research in terms of cost and effort required for discovering stories. However, the heterogeneity and veracity of data sources pose further obstacles in knowledge extraction making it a hard task to handle. The purpose of this study is threefold. Firstly, we present a platform for automated data processing in the context of Computational Journalism. We then propose a general methodology for event extraction from different data sources. Finally, we conducted a pilot implementation of our methodology for protest events extraction from news and Twitter data. Evaluation showed promising results, indicating the feasibility of our approach