Spatial Language and Constrained Connectionism
MIT Press
Drawing on ideas from cognitive linguistics, connectionism, and
perception, The Human Semantic Potential describes a
connectionist model that learns perceptually grounded semantics for natural language
in spatial terms. Languages differ in the ways in which they structure space, and
Regier's aim is to have the model perform its learning task for terms from any
natural language. The system has so far succeeded in learning spatial terms from
English, German, Russian, Japanese, and Mixtec.
The model views
simple movies of two-dimensional objects moving relative to one another and learns
to classify them linguistically in accordance with the spatial system of some
natural language. The overall goal is to determine which sorts of spatial
configurations and events are learnable as the semantics for spatial terms and which
are not. Ultimately, the model and its theoretical underpinnings are a step in the
direction of articulating biologically based constraints on the nature of human
semantic systems.
Along the way Regier takes up such substantial
issues as the attraction and the liabilities of PDP and structured connectionist
modeling, the problem of learning without direct negative evidence, and the area of
linguistic universals, which is addressed in the model itself. Trained on spatial
terms from different languages, the model permits observations about the possible
bases of linguistic universals and interlanguage variation.
Neural Network Modeling and Connectionism series
Regier
The Human Semantic Potential jetzt bestellen!
perception, The Human Semantic Potential describes a
connectionist model that learns perceptually grounded semantics for natural language
in spatial terms. Languages differ in the ways in which they structure space, and
Regier's aim is to have the model perform its learning task for terms from any
natural language. The system has so far succeeded in learning spatial terms from
English, German, Russian, Japanese, and Mixtec.
The model views
simple movies of two-dimensional objects moving relative to one another and learns
to classify them linguistically in accordance with the spatial system of some
natural language. The overall goal is to determine which sorts of spatial
configurations and events are learnable as the semantics for spatial terms and which
are not. Ultimately, the model and its theoretical underpinnings are a step in the
direction of articulating biologically based constraints on the nature of human
semantic systems.
Along the way Regier takes up such substantial
issues as the attraction and the liabilities of PDP and structured connectionist
modeling, the problem of learning without direct negative evidence, and the area of
linguistic universals, which is addressed in the model itself. Trained on spatial
terms from different languages, the model permits observations about the possible
bases of linguistic universals and interlanguage variation.
Neural Network Modeling and Connectionism series
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