14.21, Diss: Lang Acquisition: Sakas "Ambiguity..."

[LINGUIST List]

LINGUIST List:  Vol-14-21. Tue Jan 7 2003. ISSN: 1068-4875.

Subject: 14.21, Diss: Lang Acquisition: Sakas "Ambiguity..."

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1)
Date:  Tue, 31 Dec 2002 21:46:57 +0000
From:  sakas at hunter.cuny.edu
Subject:  Lang Acquisition: Sakas "Ambiguity and the Computational..."

-------------------------------- Message 1 -------------------------------

Date:  Tue, 31 Dec 2002 21:46:57 +0000
From:  sakas at hunter.cuny.edu
Subject:  Lang Acquisition: Sakas "Ambiguity and the Computational..."


New Dissertation Abstract

Institution: City University of New York
Program: Linguistics Program
Dissertation Status: Completed
Degree Date: 2000
Author: William Sakas

Dissertation Title:
Ambiguity and the Computational Feasibility of Syntax Acquisition

Dissertation URL: http://www.hunter.cuny.edu/cs/Faculty/Sakas/papers.html

Linguistic Field:
Language Acquisition, Computational Linguistics, Cognitive Science

Dissertation Director 1: Virginia Teller
Dissertation Director 2: Janet Dean Fodor
Dissertation Director 3: Cullen Schaffer
Dissertation Director 4: Robin Clark


Dissertation Abstract:

The thesis presents a framework that can be used for empirical and
formal analysis of parameter setting models of language acquisition.
Such models attempt to mirror computationally the process by which
children acquire the grammar of their native language.  Research into
formal language learning theory standardly focuses on issues of
learnability - Under what conditions is learning possible?  The thesis
contributes to the important, but under-investigated question of
feasibility - Is acquisition possible within a reasonable amount of
time and/or with a reasonable amount of work?

The proposed framework formalizes existing notions such as the rate of
parametric ambiguity and parametric expression within a generally
defined parameter space, so that different types of learning
algorithms and grammar spaces can be explored.  Two influential
learning algorithms are examined in detail: The Triggering Learning
Algorithm (Gibson and Wexler, 1994) and The Structural Triggers
Learner (Fodor, 1998).  Empirical results indicate that the Triggering
Learning Algorithm's simple hill-climbing search heuristics are
sufficient to acquire the target grammar without the learner's
consumption of an unreasonable number of input sentences when the
learning space contains a strong correlation between the similarity of
languages and the grammars that generate them.

The results also indicate that the Structural Triggers Learner's use
of structural information lying beneath the surface word order of an
input sentence allows for feasible learning when the rate of
parametric expression varies across the input sentences encountered by
the learner.  Notably, however, both models are acutely sensitive to
changes in the amount and type of ambiguity present in the domain.  A
small change in just one of the factors that contributes to the
distribution of ambiguity has a large impact

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