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

Michael Spranger, Katrien Beuls

Research output: Working paperPreprint

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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.
Original languageEnglish
Publication statusPublished - 30 Sept 2016


  • cs.CL

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