Learning Representations of Wordforms With Recurrent Networks: Comment on Sibley, Kello, Plaut, & Elman (2008)
نویسندگان
چکیده
منابع مشابه
Learning Representations of Wordforms With Recurrent Networks: Comment on
Sibley et al. (2008) report a recurrent neural network model designed to learn wordform representations suitable for written and spoken word identification. The authors claim that their sequence encoder network overcomes a key limitation associated with models that code letters by position (e.g., CAT might be coded as C-in-position-1, A-in-position-2, T-in-position-3). The problem with coding l...
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The forms of words as they appear in text and speech are central to theories and models of lexical processing, yet current means of representing wordforms are lacking in certain key aspects. In the present study, a connectionist model termed the wordformer is presented that learns wordform representations through exposure to strings of stress-marked phonemes or letters. A small-scale simulation...
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Target Papers: • William H. Wilson, A comparison of architectural alternatives for recurrent networks, Proceedings of the Fourth Australian Conference on Neural Networks, ACNN’93, Melbourne, 13 February 1993, 189-192. ftp://ftp.cse.unsw.edu.au/pub/users/billw/wilson.recurrent.ps.Z • William H. Wilson, Stability of learning in classes of recurrent and feedforward networks, in Proceedings of the ...
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ژورنال
عنوان ژورنال: Cognitive Science
سال: 2009
ISSN: 0364-0213,1551-6709
DOI: 10.1111/j.1551-6709.2009.01062.x