Example-based Complexity–Syntax and Semantics as the Production of Ad-hoc Arrangements of Examples

Example-based Complexity--Syntax and Semantics as the Production of Ad-hoc Arrangements of Examples Robert John FREEMAN rj freeman@email.com Abstract Computational linguists have traditionally sought to model language by finding underlying parameters which govern numerous examples. I describe a different approach which argues that numerous examples themselves, by virtue of their many possible arrangements, provide the only way to specify a sufficiently rich set of "parameters". Essentially I argue for a different relationship between example and parameter. With examples primary, and parameterizafions of them secondary, the real "productions". Rather than representing a redundant complexity, examples should actually be seen as a simplification, a basis for the numerous arrangements of their "parameterizations". Another way of looking at it is to say I argue arrangements of examples, rather than simply revealing underlying parameters, represent in themselves an ignored resource for the modelling of syntactic, and semantic, complexity. I have implemented a small, working, "shallow parser" based on these ideas. Introduction--Machine Learning, Data, and Parameterizations ! contrast my work with Machine Learning. There are similarities in the emphasis on the analysis of relationships among data, but there are also differences in the assumptions about the nature of the system. I think there has been a tacit assumption in Machine Learning that language system consists of underlying parameters which generate a variety of examples. My argument is that you can turn that relationship around and get a great deal more descriptive power in the form of varying parameterizations of the order in a set of examples. Under the umbrella of Machine Learning I include a wide variety of data based analyses of language which have become popular in recent years. Both distributed and statistical data based models fit in that category: back-propagation networks, Hidden Markov Models, maximum entropy parametefizafions. Apart from their emphasis on data, however, they have one thing in common, and in common with earlier symbolic attempts to codify language system. They all hypothesize parameters for distributions of data. I say it is worth considering that the essence of language is not in such underlying parameters but the collections of examples we seek them through. That there are no underlying parameters, only the chaos of example, much as is the case in a population of people (see also Kenneth Pike "analogies between linguistic structure and the structure of society", in de Beaugrande ( 1991)). One way to describe this is to say that language might be "irreducibly distributed". A system where a collection of examples is the smallest set which describes all its structure. Although there might be different levels of this independence (along with differing abilities to parameterize: viz. phonology, morphology, syntax). We might contrast irreducibly distributed systems with those which are parametrically distributed, like a letter recognition system. Certainly, however, we could contrast them with statistical, systems, where only the likelihood of the outcomes is variable. 47 R from N and the Descriptive Power of Sets The best thing about such "irreducibly distributed" systems is their power. The number of combinations of …

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