TAG's as a Grammatical Formalism for eneration

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Abstract

Tree Adjoining Grammars, or TAGV, (Joshi, Levy & Takahashi 1975; Joshi 1983; Kroch A Joshi 1985) were developed as an alternative to the standard syntactic formalisms that are used in theoretical analyses of language. They are attractive because they may provide just the aspects of context sensitive expressive power that actually appear in human languages while otherwise remaining context free. This paper describes how we have applied the theory of Tree Adjoining Grammars to natural language generation. We have been attracted to TAG'S because their central operation-the extension of an "initial" phrase structure tree through the inclusion, at very specifically constrained locations, of one or more "auxiliary" trcm contjponds directly to certain central operations of our own, performance-oriented theory. We begin by briefly describing TAG's as a formalism for phrase structure in a competence theory, and summarize the points in the theory of TAG'S that are germaine to our own theory. We then consider generally the position of a grammar within the generation piocess, introducing our use of TAG's through a contrast with how others have used systemic grammars. This takes us to the core results of our paper, using examples from our research with well-written texts from newspapers, we walk through our TAG inspired treatments cI raising and wh-movement, and show the correspondence of the TAG "adjunction" operation and our "attachment" process. In the final section we discuss extensions to the theory, motivated by the way we use the operation corresponding to TAG's' adjunction in performance. This suggests that the competence theory of TAG's can be profitably projected to structures at the morphological level as well as the present syntactic level.

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McDonald, D. D., & Pusteejovsky, J. D. (1985). TAG’s as a Grammatical Formalism for eneration. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1985-July, pp. 94–103). Association for Computational Linguistics (ACL). https://doi.org/10.3115/981210.981222

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