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- W2016157899 abstract "Abstract Lexical and pragmatic ambiguity is a major source of uncertainty in natural language understanding. Symbolic models can make high-level inferences necessary for understanding text, but handle ambiguity poorly, especially when later context requires a re-interpretation of the input. Structured connectionist networks, on the other hand, can use their graded levels of activation to perform lexical disambiguation, but have trouble performing the variable bindings and inferencing necessary for language understanding. We have previously described a structured connectionist model, ROBIN, which overcomes many of these problems and allows the massively-parallel application of a large class of general knowledge rules. This paper describes how ROBIN uses these abilities and the contextual evidence from its semantic networks to disambiguate words and infer the most plausible plan/goal analysis of the input, while using the same mechanism to smoothly re-interpret the input if later context makes an alternative interpretation more likely. We present several experiments illustrating these abilities and comparing them to those of other connectionist models, and discuss several directions in which we are extending the model." @default.
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- W2016157899 date "1992-02-01" @default.
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- W2016157899 title "Lexical and pragmatic disambiguation and re-interpretation in connectionist networks" @default.
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- W2016157899 doi "https://doi.org/10.1016/0020-7373(92)90014-c" @default.
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