Modelling constructivist language acquisition through syntactico-semantic pattern finding.

The constructivist acquisition of language by children has been elaborately documented by researchers in psycholinguistics and cognitive science. However, despite the centrality of human-like communication in the field of artificial intelligence, no faithful computational operationalizations of the mechanisms through which children learn language exist to date. In this article, we fill part of this void by introducing a mechanistic model of the constructivist acquisition of language through syntactico-semantic pattern finding.

The FCG Editor: An innovative environment for engineering computational construction grammars.

Since its inception in the mid-eighties, the field of construction grammar has been steadily growing and constructionist approaches to language have by now become a mainstream paradigm for linguistic research. While the construction grammar community has traditionally focused on theoretical, experimental and corpus-based research, the importance of computational methodologies is now rapidly increasing. This movement has led to the establishment of a number of exploratory computational construction grammar formalisms, which facilitate the implementation of construction grammars, as well as their use for language processing purposes.

An Opinion Facilitator for Online News Media.

With more and more voices and opinions entering the public domain, a key challenge facing journalists and editors is maximizing the context of the information that is presented on news websites. In this paper, we argue that systems for exposing readers to the many aspects of societal debates should be grounded in methods and tools that can provide a fine-grained understanding of these debates. The present article thereby explores the conceptual transition from opinion observation to opinion facilitation by introducing and discussing the Penelope opinion facilitator: a proof-of-concept reading instrument for online news media that operationalizes emerging methods for the computational analysis of cultural conflict developed in the context of the H2020 ODYCCEUS project.

From Continuous Observations to Symbolic Concepts: A Discrimination-Based Strategy for Grounded Concept Learning.

Autonomous agents perceive the world through streams of continuous sensori-motor data. Yet, in order to reason and communicate about their environment, agents need to be able to distill meaningful concepts from their raw observations. Most current approaches that bridge between the continuous and symbolic domain are using deep learning techniques.