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

Abstract

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.

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. IEEE