Abstract
0. Introduction The purpose of this paper is to investigate methodologies that get at speakers’ tacit knowledge of structure in polysynthetic languages. As linguists we need theoretical structures that both link languages to other languages and are internally cohesive. Polysynthetic languages pose a particular challenge to linguistic theories because of the internal complexity of the word. Polysynthesis generally refers to language types in which the verbal structure is semantically rich, morphologically complex with an opaque morpheme structure. In polysynthetic languages, word formation is often characterized as an often highly abstract concatenation of virtual morphemes that are conditioned by post hoc morphophonemic rules. However in light of their learnability and their overall long-term stability as polysynthetic, a more parsimonious model is likely to have an advantage over abstract models where internal structure is obscure. Adding to the problem, crucially, given that polysynthetic languages as a rule are often severely under-documented, the availability of data on polysynthesis in no way is comparable to that found with better-studied languages like English, which places us at a severe disadvantage in our understanding of them. With pre-theoretic assumptions in place concerning the nature of polysynthesis, the models resulting from research naturally tend toward translations of polysynthesis into analytic frameworks. We consider such approaches to be limited in their productivity and insight; the rest of this paper focuses on the development of new methodologies for the investigation of polysynthetic languages using Navajo, a canonical example of a polysynthetic language as our test case, expandable to the Athabaskan family. Some of the essential issues presented by polysynthesis are: the nature of lexical productivity, the structure of a verb-based lexicon, the nature of evidence for theory, techniques of language documentation, and the nature of language change in lexically complex languages.
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