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- W2170702935 abstract "We describe a neural network, called RuleNet, that learns explicit, symbolic condition-action rules in a formal string manipulation domain. RuleNet discovers functional categories over elements of the domain, and, at various points during learning, extracts rules that operate on these categories. The rules are then injected back into RuleNet and training continues, in a process called iterative projection. By incorporating rules in this way, RuleNet exhibits enhanced learning and generalization performance over alternative neural net approaches. By integrating symbolic rule learning and subsymbolic category learning, RuleNet has capabilities that go beyond a purely symbolic system. We show how this architecture can be applied to the problem of case-role assignment in natural language processing, yielding a novel rule-based solution." @default.
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- W2170702935 date "1991-12-02" @default.
- W2170702935 modified "2023-09-25" @default.
- W2170702935 title "Rule Induction through Integrated Symbolic and Subsymbolic Processing" @default.
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