Output

“The ultimate purpose of social sciences is to furnish causal explanations of classes of observable events, which are, at least in part, generated by individual and collective agency/action.” [1] The aim of a digital assistant for social history research is therefore to support social scientists with the construction of such causal explanations for observable events, also theories or hypotheses. 

It is widely accepted that humans construct narratives to make sense of complex issues in their lives and society. If we want to build machines that are capable of truly understanding such narratives, we need to be able to reliably identify and track the characters and other entities that play a role in a narrative. For example, if a person writes: “The boy likes The Witches. The book was written by Roald Dahl.”

Constructionist approaches to language make use of form-meaning pairings, called constructions, to capture all linguistic knowledge that is necessary for comprehending and producing natural language expressions. Language processing consists then in combining the constructions of a grammar in such a way that they solve a given language comprehension or production problem.

The domain of inequality is heavily characterized by causal claims. Multiple researchers in economics, social sciences and humanities have studied the sources of modern inequality. However, modelling their automatic detection in natural language is still an open issue. This research project aims to tackle this gap by detecting explicit causal relationships in Italian texts related to inequality.

IRL, developed by Luc Steels and collaborators, is a parsing technique that captures the semantics of a natural language expression as a network of logical constraints. Determining the meaning of a sentence then amounts to finding consistent assignments of variables that satisfy these constraints.

The main objective of this miniproject is to identify a design solution for MUHAI’s data ecosystem and for the integration of its components. MUHAI, is a project that focuses on human-machine understanding and cooperation, by means of mimicking in machines how humans make sense of experiences.