Technical Report Available

[Dr Michael G Dyer]

Symbolic NeuroEngineering for Natural Language Processing:
A Multilevel Research Approach.

Michael G. Dyer

Tech. Rep. UCLA-AI-88-14

Abstract:

Natural language processing (NLP) research has been built on the assumption
that natural language tasks, such as comprehension, generation, argumentation,
acquisition, and question answering, are fundamentally symbolic in nature.
Recently, an alternative, subsymbolic paradigm has arisen, inspired by
neural mechanisms and based on parallel processing over distributed
representations.  In this paper,  the assumptions of these two paradigms are
compared and contrasted, resulting in the observation that each paradigm
possesses strengths exactly where the other is weak, and vice versa.  This
observation serves as a strong motivation for synthesis.  A multilevel
research approach is proposed, involving the construction of hybrid models,
to achieve the long-term goal of mapping high-level cognitive function into
neural mechanisms and brain architecture.  Four levels of modeling are
discussed:  knowledge engineering level, localist connectionist level,
distributed processing level, and artificial neural systems dynamics level.
The two major goals of research at each level are (a) to explore its scope
and limits and (b) to find mappings to the levels above and below it.  In this
paper the capabilities of several NLP models, at each level, are described,
along with major research questions remaining to be resolved and major
techniques currently being used in an attempt to complete the mappings.
Techniques include:  (1) forming hybrid systems with spreading activation,
thresholds and markers to propagate bindings, (2) using extended back-error
propagation in reactive training environments to eliminate microfeature
representations, (3) transforming weight matrices into patterns of activation
to create virtual semantic networks, (4) using conjunctive codings to 
implement role bindings, and (5) employing firing patterns and time-varying
action potential to represent and associate verbal with visual sequences.

(This report to appear in J. Barnden and J. Pollack (Eds.) Advances in
Connectionist and Neural Computation Theory. Ablex Publ.  An initial 
version of this report was presented at the AAAI & ONR sponsored Workshop
on HIgh-Level Connectionism, held at New Mexico State University, April
9-11, 1988.)

For copies of this tech. rep.,  please send requests to:
Valerie at CS.UCLA.EDU
or
Valerie Aylett
3532 Boelter Hall
Computer Science Dept.
UCLA, Los Angeles, CA 90024