Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces

This paper discusses lexicon word learning in high-dimensional meaning spaces from the viewpoint of referential uncertainty. We investigate various state-of-the-art Machine Learning algorithms and discuss the impact of scaling, representation and meaning space structure. We demonstrate that current Machine Learning techniques successfully deal with high-dimensional meaning spaces. In particular, we show that exponentially increasing dimensions linearly impact learner performance and that referential uncertainty from word sensitivity has no impact.Comment: Published as Spranger, M. and Beuls, K. (2016). Referential uncertainty and word learning in high-dimensional, continuous meaning spaces. In Hafner, V. and Pitti, A., editors, Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on, 2016. IEE

Text Categorization for Intellectual Property

This study investigates the effect of training different categorization algorithms on various patent document representations

Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning

In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL). If successful, future language game experiments will benefit from the rapid and promising methodological advances in the MARL community, while future MARL experiments on learning emergent communication will benefit from the insights and results gained from language game experiments. We strongly believe that this cross-pollination has the potential to lead to major breakthroughs in the modelling of how human-like languages can emerge and evolve in multi-agent systems.Comment: This paper was accepted for presentation at the 2020 AAAI Spring Symposium `Challenges and Opportunities for Multi-Agent Reinforcement Learning' after a double-blind reviewing proces

Construction Grammar and Artificial Intelligence

In this chapter, we argue that it is highly beneficial for the contemporary construction grammarian to have a thorough understanding of the strong relationship between the research fields of construction grammar and artificial intelligence. We start by unravelling the historical links between the two fields, showing that their relationship is rooted in a common attitude towards human communication and language. We then discuss the first direction of influence, focussing in particular on how insights and techniques from the field of artificial intelligence play an important role in operationalising, validating and scaling constructionist approaches to language. We then proceed to the second direction of influence, highlighting the relevance of construction grammar insights and analyses to the artificial intelligence endeavour of building truly intelligent agents. We support our case with a variety of illustrative examples and conclude that the further elaboration of this relationship will play a key role in shaping the future of the field of construction grammar.Comment: Peer-reviewed author's draft of a chapter to appear in the Cambridge Handbook of Construction Grammar (2024 - edited by Mirjam Fried and Kiki Nikiforidou