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.
Cite
@article{arxiv.1609.09580,
title = {Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces},
author = {Michael Spranger and Katrien Beuls},
journal= {arXiv preprint arXiv:1609.09580},
year = {2016}
}
Comments
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