Nominal Structures and Structural Recursion

It is possible within Tree Adjoining Grammar to reproduce many of the syntactic analyses originally formulated by linguists in transformational terms. To the extent that these analyses are well-motivated empirically, this fact makes TAG interesting for use in developing computational learning and processing models (Joshi 1990, Frank 1992, Rambow 1994), since the use of other non-transformational formalisms sometimes forces choices of linguistic description di erent from those ordinarily made by descriptive syntacticians. Thus, using TAG, one can take advantage in the construction of parsers and learners of the computational tractability of a mathematically restrictive formalism without having to reinvent empirical syntax in order to do so. At the same time, TAG analyses are not identical in every detail to their transformational counterparts; and it is interesting to compare them where they diverge. The di erences arise because of a fundamental di erence in the way that syntactic recursion is treated in the two frameworks. In TAG, recursive structures are generated by composing elementary syntactic objects, with the result that recursion is factored apart from the representation of local syntactic dependencies. By contrast, in transformational grammar, as in many other frameworks, recursive structure and local dependencies are represented together in a single, full representation of a complex sentence. Because of this di erence in the treatment of recursion, it often turns out, when TAG is used to emulate a transformational analysis, that the TAG version has advantages, of both elegance and empirical coverage, over the original. This paper is a demonstration in a new empirical domain, that of nominal constructions, of the advantages of TAG-based syntax. By presenting a linguistically detailed account of these constructions and showing the advantages of using TAG to analyze them, we strengthen the case for the use of mathematically constrained and computationally tractable representational systems in competencebased as well as in computational linguistics.