Steedman, Mark

Sorace, Antonella

Crocker, Matthew

Keller, Frank

2005-03-21T10:23:30Z

2005-03-21T10:23:30Z

2001-03

Institute for Communicating and Collaborative Systems

This thesis deals with gradience in grammar, i.e., with the fact that some linguistic structures are not fully acceptable or unacceptable, but receive gradient linguistic judgments. The importance of gradient data for linguistic theory has been recognized at least since Chomsky's Logical Structure of Linguistic Theory. However, systematic empirical studies of gradience are largely absent, and none of the major theoretical frameworks is designed to account for gradient data. The present thesis addresses both questions. In the experimental part of the thesis (Chapters 3-5), we present a set of magnitude estimation experiments investigating gradience in grammar. The experiments deal with unaccusativity/unergativity, extraction, binding, word order, and gapping. They cover all major modules of syntactic theory, and draw on data from three languages (English, German, and Greek). In the theoretical part of thesis (Chapters 6 and 7), we use these experimental results to motivate a model of gradience in grammar. This model is a variant of Optimality Theory, and explains gradience in terms of the competition of ranked, violable linguistic constraints. The experimental studies in this thesis deliver two main results. First, they demonstrate that an experimental investigation of gradient phenomena can advance linguistic theory by uncovering acceptability distinctions that have gone unnoticed in the theoretical literature. An experimental approach can also settle data disputes that result from the informal data collection techniques typically employed in theoretical linguistics, which are not well-suited to investigate the behavior of gradient linguistic data. Second, we identify a set of general properties of gradient data that seem to be valid for a wide range of syntactic phenomena and across languages. (a) Linguistic constraints are ranked, in the sense that some constraint violations lead to a greater degree of unacceptability than others. (b) Constraint violations are cumulative, i.e., the degree of unacceptability of a structure increases with the number of constraints it violates. (c) Two constraint types can be distinguished experimentally: soft constraints lead to mild unacceptability when violated, while hard constraint violations trigger serious unacceptability. (d) The hard/soft distinction can be diagnosed by testing for effects from the linguistic context; context effects only occur for soft constraints; hard constraints are immune to contextual variation. (e) The soft/hard distinction is crosslinguistically stable. In the theoretical part of the thesis, we develop a model of gradient grammaticality that borrows central concepts from Optimality Theory, a competition-based grammatical framework. We propose an extension, Linear Optimality Theory, motivated by our experimental results on constraint ranking and the cumulativity of violations. The core assumption of our model is that the relative grammaticality of a structure is determined by the weighted sum of the violations it incurs. We show that the parameters of the model (the constraint weights), can be estimated using the least square method, a standard model fitting algorithm. Furthermore, we prove that standard Optimality Theory is a special case of Linear Optimality Theory. To test the validity of Linear Optimality Theory, we use it to model data from the experimental part of the thesis, including data on extraction, gapping, and word order. For all data sets, a high model fit is obtained and it is demonstrated that the model's predictions generalize to unseen data. On a theoretical level, our modeling results show that certain properties of gradient data (the hard/soft distinction, context effects, and crosslinguistic effects) do not have to be stipulated, but follow from core assumptions of Linear Optimality Theory.

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http://hdl.handle.net/1842/744

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University of Edinburgh. College of Science and Engineering. School of Informatics.

gradience

grammaticality

linguistic judgements

linguistic data

optimality theory

magnitude estimation

computational modeling

Gradience in Grammar: Experimental and Computational Aspects of Degrees of Grammaticality

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy