Communications of the ACM - Special issue on parallelism
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An efficient way to learn English grapheme-to-phoneme rules automatically
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Filtering obfuscated email spam by means of phonetic string matching
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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The authors study the principles governing the power and efficiency of the default hierarchy, a system of knowledge acquisition and representation. The default hierarchy trains automatically, yet yields a set of rules which can be easily assessed and analyzed. Rules are organized in a hierarchical structure containing general (default) and specific rules. In training the hierarchy, general rules are learned before specific rules. In using the hierarchy, specific rules are accessed first, with default rules used when no specific rules apply. The main results concern the properties of the default hierarchy architecture, as revealed by its application to English pronunciation. Evaluating the hierarchy as a pronouncer of English, the authors find that its rules capture several key features of English spelling. The default hierarchy pronounces English better than the neural network NETtalk, and almost as well as expert-devised systems.