Combining empirical and formal methods, we study the role causality plays in the interpretation of natural language, reasoning and decision making.

We try to understand the computational principles underlying natural language understanding by humans and machines, the neural implementation of these principles, their evolutionary origins and their usefulness in language technology.

We aim to create a new sound and transparent computational methodology to trace the history of ideas, combining techniques from symbolic and non-symbolic AI.

We carry out research at the interface of computational linguistics, cognitive science and AI to study language in interaction. Topics include: semantics & pragmatics, visually grounded language, conversational agents, language variation & change.

Our research aims to complement existing approaches to argumentation with linguistically advanced models of argument processing, assessment, and production.

We investigate language and communication in autistic individuals. This includes developing new (experimental) methods to adequately investigate the diverse autistic population across the life-span.

We investigate language and literacy acquisition in typically developing children as well as children with language-related cognitive disorders.

We focus on information access from natural language data. Our work ranges from basic research in natural language processing to key applications in human language technology.

We develop machine learning methods for natural language processing, especially for semantic tasks such as question answering, information extraction, and semantic parsing. We also work on interpretability and controlability of deep learning models.

Our research is in the area of natural language processing, with a specific focus on computational semantics and machine learning from linguistic and multimodal data.

People often reason contrary to the prescriptions of classical logic. We study such cases and hypothesise that they are a consequence of a tendency in human cognition to neglect empty representations.

We work on probabilistic models for natural language processing, with particular emphasis on neural machine translation and interpretability of deep learning models.

SignLab is a cross-faculty research lab bringing together a long tradition of sign language linguistics in Amsterdam with recent advances in artificial intelligence.

Our research focus lies in computationally modelling the production, comprehension, acquisition and diachronic development of phonology and phonetics, and in supply experimental evidence for those.

Our research concentrates on statistical models for structured language processing with application to machine translation, paraphrasing, semantic and morpho-syntactic parsing, and statistical learning for NLP.

The Visualisation Lab provides access to facilities and resources for students and researchers who want to work on interactive visual systems. The lab specialises on scientific visualisation, high-performance interactive graphics and XR/MR/VR/AR.