Add to ORKGAbstract: I consider the problems that neural networks face when learning from (or responding to) ambiguous data. The general discussion is illustrated throughout with a range of language processing examples. 1
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated f...
Traditional approaches to language processing have been based on explicit, discrete representations ...
This book provides students and researchers of bilingualism with the most recent methodological and ...
S. Joordens and D. Besner (1994) described an attempt to simulate a semantic ambiguity advantage in ...
260 pagesThe majority of work at the intersection of computational linguistics and natural language ...
Connectionist modeling (AKA neural network modeling, connectionism) is rapidly becoming a dominant d...
In the past twenty years the connectionist approach to language development and learning has emerged...
If we want to explain cognitive processes with means of connectionist networks, these networks have ...
Traditional approaches to language processing have been based on explicit, discrete representations ...
This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for...
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated f...
The field of formal linguistics was founded on the premise that language is mentally represented as ...
This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for ...
In the present study, I was interested in the neurofunctional representation of ambiguity processing...
Decades of studies trying to define the extent to which artificial neural networks can exhibit syste...
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated f...
Traditional approaches to language processing have been based on explicit, discrete representations ...
This book provides students and researchers of bilingualism with the most recent methodological and ...
S. Joordens and D. Besner (1994) described an attempt to simulate a semantic ambiguity advantage in ...
260 pagesThe majority of work at the intersection of computational linguistics and natural language ...
Connectionist modeling (AKA neural network modeling, connectionism) is rapidly becoming a dominant d...
In the past twenty years the connectionist approach to language development and learning has emerged...
If we want to explain cognitive processes with means of connectionist networks, these networks have ...
Traditional approaches to language processing have been based on explicit, discrete representations ...
This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for...
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated f...
The field of formal linguistics was founded on the premise that language is mentally represented as ...
This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for ...
In the present study, I was interested in the neurofunctional representation of ambiguity processing...
Decades of studies trying to define the extent to which artificial neural networks can exhibit syste...
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated f...
Traditional approaches to language processing have been based on explicit, discrete representations ...
This book provides students and researchers of bilingualism with the most recent methodological and ...