Drawing on ideas from cognitive linguistics, connectionism, and perception, The Human Semantic Potential describes a connectionist model that learns perceptuallygrounded semantics for natural language in spatial terms. Languages differ in the ways in which theystructure space, and Regier's aim is to have the model perform its learning task for terms from anynatural 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-dimensionalobjects moving relative to one another and learns to classify them linguistically in accordance withthe spatial system of some natural language. The overall goal is to determine which sorts of spatialconfigurations 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 articulatingbiologically based constraints on the nature of human semantic systems.
Along theway Regier takes up such substantial issues as the attraction and the liabilities of PDP andstructured connectionist modeling, the problem of learning without direct negative evidence, and thearea of linguistic universals, which is addressed in the model itself. Trained on spatial terms fromdifferent languages, the model permits observations about the possible bases of linguisticuniversals and interlanguage variation.
Neural Network Modeling andConnectionism series
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