کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410284 | 679134 | 2011 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modelling vocabulary acquisition, adaptation and generalization in infants using adaptive Bayesian PLSA
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 74, Issue 11, May 2011, Pages 1874–1882
Journal: Neurocomputing - Volume 74, Issue 11, May 2011, Pages 1874–1882
نویسندگان
J. Driesen, H. Van hamme,