From text to Deep Data

Project coordinator of this project whichs develops a model that provides a representation of things in the (real or assumed) world and allows us to indicate the perspective of different sources on them. In other words, we aim to provide a framework that can represent what is said about a topic, a person or an event and how this is said in and by various sources, making it possible to place alternative perspectives next to each other. We develop software to detect these perspectives in texts and represent the output according to our formal model which is called GRaSP (Grounded Representation and Source Perspective). GRaSP is an overarching model that provides the means to: (1) represent instances (e.g. events, entities) and propositions in the (real or assumed) world, (2) to relate them to mentions in text using the Grounded Annotation Framework, and (3) to characterize the relation between mentions of sources and targets by means of perspective-related annotations such as attribution, factuality and sentiment.