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NOTA

 

Project Name: NOTA: Non-Traditional Approaches

Abbreviation: NOTA

Start date: January 1, 1998

End date: February 1, 2000

Project Description:

Summary

The project serves a twofold purpose, on the one hand the applications of neural networks in the field of natural language processing. Here the main interest is performance simulation; what could be main contributions of neural networks to natural language processing, especially to grammar inference and word disambiguation. On the other hand the goal is to develop design strategies for neural networks. Globally there are three important stages in the design traject:
  • An initial problem specification tailored towards neural networks.
  • The transformation of the problem specification towards a neural prototype.
  • An efficient neural implementation of the prototype.
In all these stages compositionality and modularity play an important role.

Objectives

The objectives are to study the competence of neural networks for certain natural language tasks such as grammar inference, disambiguation etc. A special research direction is the application of Hopfield networks for language recognition. The second important theme in the project is the development of design strategies from a given problem specification towards an efficient neural network solution up to tolerable faults. First a specification formalism should be developed which is tailored towards neural network implementations. Secondly one needs to transform this specification into a neural prototype. And finally this prototype must be transformed in to an efficient neural network which satisfies the initial problem specification.

Motivation

Recently there is a new/renewed interest in approaches to information and language processing that are not based on the traditional Von Neumann architecture or that do not fit into the usual paradigms in computing (imperative, functional, logic, object-oriented programming). Typical examples of these approaches include:
  • neural networks (NN),
  • genetic algorithms (GA),
  • cellular automata (CA),
  • computation based on fuzzy logic (FL),
Although these computing models have been inspired by quite different ideas (How does the human brain function? What is the behaviour of populations evolving in time? Do self-reproducing automata exist and are they universal? Can we compute in a more subtle way than just using the crude "yes" (1) and "no" (0) as basic values only?, respectively) nowadays there emerge some central themes and common features.
  • All these models belong to that area of science that is usually referred to as complex, dynamical or non-linear systems. These three different names corresponds to several aspects of the subject under consideration. Restricting ourselves to the subject "information processing", we first remark that each computational process that involves a basic programming concept like "if then else fi" is in essence non-linear. Of course, our subject is complex (Nobody wants to be involved in trivial matters only!): the reason is that we usually study a large number of relatively simple information processing elements that are interconnected in some regular fashion in order to obtain an interesting global computational phenomenon. Keywords, usually mentioned in this context, are connectionism and massively parallelism. Thirdly, the adjective "dynamical" emphasises the fact that we are interested in the behaviour of the computational process as a function of the parameter time rather than the mere final result of such a computation.
  • There are interesting connections to combinatorial optimisation: from a certain point of view NN's and GA's (as well as the Boltzmann machine and simulated annealing) are stochastic optimisation techniques.
  • Apart from being defined locally (e.g. the definitions of the neurons (NN), the chromosomes (GA), the cells (CA)) the overall computational process may be controlled at a global level ("training the neural network'' i.e. adjusting the weights in the interconnections, manipulating the fitness' function in GA).
In this field we will focus our attention to a few fundamental problems and to some application areas. These fundamental questions include:
  1. Given the properties of the simple processing elements (the local properties), what is the global behaviour of a large complex of these regularly interconnected processing elements?
  2. Given a particular computational problem, i.e. the given global behaviour of our complex system, what is the best way to design the local processing elements and to control these elements at a global level in order to solve this computational problem. In other words: given a specification of the computational problem, try to specify the global behaviour of the system, derive the local behaviour from this latter specification in such a way that the original problem will be solved efficiently. This research includes the development of formalisms for specifying the global behaviour (especially for NN's) as well as transformations of specifications to equivalent, more efficient ones.

Project-coordinator

The following HMI-member(s) is/are coordinator of this Project

Dirk Heylen

 

Publications

Here you can find the publications

 

 

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