A Connectionist Symbol ManipulatorThat Discovers the Structure ofContext - Free

A Connectionist Symbol Manipulator that Discovers the Structure of Context-Free Languages

We present a neural net architecture that can discover hierarchical and recursive structure in symbol strings. To detect structure at multiple levels, the architecture has the capability of reducing symbols substrings to single symbols, and makes use of an external stack memory. In terms of formal languages, the architecture can learn to parse strings in an LR(O) contextfree grammar. Given trai...

Identi ability in the Limit ofContext - Free

Symbol Structures in Connectionist Networks: Five Properties and Two Architectures

We define five properties of symbol representations (called the Five M's) that together provide a powerful and flexible symbol processing facility. Our ideal symbol structures arc Mobile, Memorable, Meaningful, Malleable, and Modifiable. Conventional symbol processing architectures, such as Lisp on von Neumann machines, support some of these properties, but others seem to require flexibility an...

The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model

Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic approaches and on connectionist only systems. This paper presents new simulations on the use of neural network architectures for the grounding of symbols on categories. In particular, the connectivity patterns between layers of the ...

Learning grammars with recurrent neural networks

Most of language acquisition models that have been constructed so far are based on traditional AI approaches. On the other hand, artificial neural networks (ANNs), contrasting with traditional AI approaches, have many great abilities such as ability to learn, generalization capability and robustness. But they are poor in representing compositional structures and manipulating them, and are consi...