Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663079
Title: Dynamic syntax
Author: Tugwell, David
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1999
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Abstract:
This thesis presents a model of natural language syntax as a dynamic system, defining possible transitions between a set of states. The model is inherently left-to-right in nature, with the states representing the growing interpretation of a sentence, and syntactic rules specifying the way the interpretation can be incremented given the next word in the string. In the first part of the thesis, I address the question of the use of models in linguistics. Accepting the standard arguments for the modularity of the process of language comprehension, I argue nevertheless that a model of syntactic competence is only open to objective evaluation if it is embedded in an overall model of performance. I argue that a dynamic formulation of the competence grammar ensures a transparent relation to what is known about language comprehension, in particular its incremental nature. I show that this obviates the need for a level of independent syntactic structure (either constituent or dependency-based), and is thus maximally parsimonious. In the proposed model, syntactic rules do not form an autonomous system, but make direct reference to the growing interpretation, thus distinguishing the model from other incremental approaches. In the second part, I go on to examine a wide-range of syntactic constructions, predominantly in English, and discuss how they may be modelled in the dynamic system. This includes a range of unbounded dependency constructions, problems of complement control, coordination and syntactic constraints on the binding of anaphora. In the final part, I look at the issues involved in embedding the dynamic model of syntactic competence in a model of language comprehension, seen both as a practical tool and as a model of the human sentence processor. I discuss how a probabilistic language model may be created by training the model of syntax on pre-analyzed data. Finally, I consider the consequences of possible processing strategies and how to model limitations on the human processor.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.663079  DOI: Not available
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