Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.644994
Title: A statistical model of human lexical category disambiguation
Author: Corley, N. S. J.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1997
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Abstract:
Research in Sentence Processing is concerned with discovering the mechanism by which linguistic utterances are mapped onto meaningful representations within the human mind. Models of the Human Sentence Processing Mechanism (HSPM) can be divided into those in which such mapping is performed by a number of limited modular processes and those in which there is a single interactive process. A further, and increasingly important, distinction is between models which rely on innate preferences to guide decision processes and those which make use of experience-based statistics. In this context, the aims of the current thesis are two-fold: - To argue that the correct architecture of the HSPM is both modular and statistical - the Modular Statistical Hypothesis (MSH). - To propose and provide empirical support for a position in which human lexical category disambiguation occurs within a modular process, distinct from syntactic parsing and guided by a statistical decision process. Arguments are given for why a modular statistical architecture should be preferred on both methodological and rational grounds. We then turn to the (often ignored) problem of lexical category disambiguation and propose the existence of a pre-syntactic Statistical Lexical Category Module (SLCM). A number of variants of the SLCM are introduced. By empirically investigating this particular architecture we also hope to provide support for the more general hypothesis - the MSH. The SLCM has some interesting behavioural properties; the remainder of the thesis empirically investigates whether these behaviours are observable in human sentence processing. We first consider whether the results of existing studies might be attributable to SLCM behaviour. Such evaluation provides support for an HSPM architecture that includes this SLCM and allows us to determine which SLCM variant is empirically most plausible. Predictions are made, using this variant, to determine SLCM behaviour in the face of novel utterances; these predictions are then tested using a self-paced reading paradigm. The results of this experimentation fully support the inclusion of the SLCM in a model of the HSPM and are not compatible with other existing models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.644994  DOI: Not available
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