Modelling vocabulary acquisition, adaptation and generalization in infants using adaptive Bayesian PLSA

Authors:
J. Driesen;H. Van hamme
Affiliations:
Department ESAT, K.U.Leuven, Leuven, Belgium;Department ESAT, K.U.Leuven, Leuven, Belgium
Venue:
Neurocomputing
Year:
2011

Citing 3
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Abstract

During the early stages of language acquisition, young infants face the task of learning a basic vocabulary without the aid of prior linguistic knowledge. Attempts have been made to model this complex behaviour computationally, using a variety of machine learning algorithms, a.o. non-negative matrix factorization (NMF). In this paper, we replace NMF in a vocabulary learning setting with a conceptually similar algorithm, probabilistic latent semantic analysis (PLSA), which can learn word representations incrementally by Bayesian updating. We further show that this learning framework is capable of modelling certain cognitive behaviours, e.g. forgetting, in a simple way.