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LINGUIST List 18.252

Wed Jan 24 2007

Diss: Computational Ling/Phonology: Bíró: 'Finding the Right Words:...'

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        1.    Tamás Bíró, Finding the Right Words: Implementing Optimality Theory with Simulated Annealing

Message 1: Finding the Right Words: Implementing Optimality Theory with Simulated Annealing
Date: 24-Jan-2007
From: Tamás Bíró <birotnytud.hu>
Subject: Finding the Right Words: Implementing Optimality Theory with Simulated Annealing

Institution: Rijksuniversiteit Groningen
Program: Alpha-Informatica
Dissertation Status: Completed
Degree Date: 2006

Author: Tamás Bíró

Dissertation Title: Finding the Right Words: Implementing Optimality Theory with Simulated Annealing

Dissertation URL: http://dissertations.ub.rug.nl/faculties/arts/2006/t.s.biro/

Linguistic Field(s): Computational Linguistics

Dissertation Director:
Gosse Bouma
Dicky Gilbers
John Nerbonne

Dissertation Abstract:

This dissertation presents an implementation of Optimality Theory (OT) that
also aims at accounting for certain variations in speech. The Simulated
Annealing for Optimality Theory Algorithm (SA-OT, Fig. 2.8, on page 64)
combines OT with simulated annealing, a widespread heuristic optimisation
technique. After a general introduction to Optimality Theory and the
discussion of certain 'philosophical background questions' (especially on
the role of probabilities in linguistics; Chapter 1), the SA-OT Algorithm
is introduced (informally in section 2.2, mathematically in sections 3.3
and 3.4), put into a broader context (section 2.1, Chapter 4, and sections
8.2 and 8.3), and experimented with (section 2.3, Chapters 5-7).

As section 2.1 argues, heuristic algorithms-- such as SA-OT-- may serve as
adequate models of the computations performed by the human brain for at
least three reasons: (1) many of these algorithms are simple, (relatively)
effcient and produce some output within a predefined time span, even if (2)
they may make errors, and finally (3) the algorithm can be speeded up with
a price to be paid in reduced precision. A faster computation is possible,
but more prone to make errors. The adequacy of such a model is corroborated
if besides the grammatical forms it also reproduces the empirically
observable error patterns under different conditions. Importantly, these
predictions are quantitative, and the algorithm's parameters can
'fine-tune' the output frequencies of the erroneous or alternating forms.

Therefore, SA-OT is claimed to be a model of linguistic performance. Table
2.1 (page 43) formulates this idea: by distinguishing between a linguistic
model and its implementation, one can account for both linguistic
competence and certain types of linguistically motivated performance
phenomena. Thus an adequate linguistic model (a grammar, such as a
well-founded OT grammar) predicts correctly which forms are judged as
grammatical by the native speaker. This layer refers to the static
knowledge of the language in the native speaker's brain. On top of that is
built the implementation of the grammar as a model of the dynamic language
production process.

In particular, SA-OT requires a topology (a neighbourhood structure) on the
OT candidate set. Consequently, the notion of a local optimum is
introduced: a candidate that is more harmonic than all its neighbours is a
local optimum, independently of whether it is the most harmonic element of
the entire candidate set. Local optima are the candidates that can emerge
as outputs in SA-OT. The global optimum predicts the grammatical form,
whereas all other outputs should model performance errors.

The second part of the dissertation experiments with SA-OT, introduces a
few techniques and tricks, and analyzes the role of its parameters. For
that purpose, the following phonological phenomena are modelled: metrical
stress shifts in Dutch fast speech, regressive and progressive voice
assimilation, cliticization of the Hungarian definite article and

A longer summary can be found at:

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