One area in which artificial neural networks (ANNs) may strengthen NLP systems is in the identification of words under noisy conditions. In order to achieve this benefit when spelling errors or spelling variants are present, variable-length strings of symbols must be converted to ANN input/otttput form--fixed-length arrays of numbers. A common view in the neural network community has been that different forms of input/output representations have negligible effect on ANN performance. This paper, however, shows that input/output representations can in fact affect the performance of ANNs in the case of natural language words. Minimum properties for an adequate word representation are proposed, as well as new methods of word representation. To test the hypothesis that word rep...

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