LINGUIST List 14.2722

Thu Oct 9 2003

Review: General Ling: Bod, Hay & Jannedy (2003)

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  1. Azra Nahid Ali, Probabilistic Linguistics

Message 1: Probabilistic Linguistics

Date: Thu, 09 Oct 2003 01:21:28 +0000
From: Azra Nahid Ali <>
Subject: Probabilistic Linguistics

Bod, Rens, Jennifer Hay and Stefanie Jannedy (2003) Probabilistic
Linguistics, MIT Press, A Bradford book.

Announced at

Azra N. Ali, School of Computing and Engineering, University of
Huddersfield, England.


The study of language has very much been categorical, however we are
now in an era where we cannot ignore that language shows probabilistic
properties and the book clearly deals with this. The book is all
about probabilistic linguistics and each chapter covers probabilistic
modeling from a different theoretical linguistic view; from
sociolinguistics to phonology. The book begins with an introductory
chapter about probabilistic linguistics and probability theory before
delving deep into probabilistic linguistics. Each theoretical chapter
is covered by a specialist in the field. The book also has a glossary
which is well documented and if you didn't know what 'hypothesis'
meant, well you do now.

Chapter 1: Introduction (by Rens Bod, Jennifer Hay, and Stefanie

The chapter provides an overview of probabilistic linguistics and how
probability plays a role in linguistics, showing with examples that
not all linguistic is categorical, in fact it is gradient and shows
probability properties. You can quickly grasp how frequency and
probability fits together in linguistic theory/approaches before even
reading any further.

Chapter 2: Elementary Probability Theory (by Rens Bod) 

This is an introductory chapter on probability theory. The chapter
starts off with simple linguistic examples (general probability
calculations, joint and conditional probability) that all linguistic
readers should be able to understand, before moving on to more complex
examples - probabilistic grammars (probabilistic context free grammars
and data- oriented parsing models) but still within ease of

Chapter 3 - Probabilistic Modeling in Psycholinguistics (by Dan

While we may fail to see probabilistic properties in linguistics, Dan
clearly highlights where they can be found and at the same time
provides a good literature support.

Jurafsky introduces the chapter by talking about frequency, showing
that the cognitive processing time is considerably short for high
frequency words than for low frequency words. He explains that high
frequency words have a shorter duration time and often the final coda
of a word is unstable, where deletion of /d/ and /t/ are apparent. He
then moves on to neighbouring words in a sentence where the
probability is an important aspect in speech comprehension and
production, followed by a different form of frequencies - 'Syntactic
subcategorization Frequencies' of verbs. In this section, conditional
probability is discussed at some length.

The latter half of the chapter discusses 'Probabilistic Architectures
and Model'. The section details different types of probabilistic
models for sentence processing, for example, constraint-based models,
competition model, Markov models, stochastic context-free grammars,
and Bayesian belief networks. Each model is described in detail with
examples and weaknesses of the models also highlighted.

Chapter 4 - Probabilistic Sociolinguistics (by Norma Mendoza- Denton,
Jennifer Hay, and Stefanie Jannedy)

The chapter provides a good introduction to sociolinguistics variation
and points out how existing statistical techniques are poor and not
suitable for analysing sociolinguistics data. Traditional statistical
methods cannot be used by the sociolinguistics researcher because
statistical techniques like Analysis of Variance (ANOVA) require
controlled data for their use. The chapter discusses the need for
more advanced multivariate probabilistic methods and shows how such
techniques can be used to analyse sociolinguistics variation data.

The probabilistic techniques that are discussed are related to one
particular language variation case - the monophthongization of /ay/
which is apparent in African- American speakers in the southern states
of U.S. Data are analysed, first by using the traditional frequency
approaches then moving on to the VARBRUL program and Classification
and Regression Tress (CART).

VARBRUL program is a form of logistic regression model and the author
details the framework of VARBRUL and discusses how this program
compares with the commercial applications like SPSS and SAS. Latter
half of the chapter illustrates how VARBRUL program can be used to
collect and analyse data - monophthongization of /ay/ in Oprah
Winfrey's speech. Oprah Winfrey is an African-American talk-show host
and the program is used to analyse the considerable style shifting
that is apparent in her speech. In the final section, CART approach is
used to investigate patterns in the data.

Chapter 5 - Probability in Language Change (by Kie Zuraw) 

The chapter looks at the role that probability plays to address the
issue of language change. Language changes over time and this is
apparent in the changes of observed probabilities over time. Zuraw
shows that by applying probabilistic approaches to language change, it
enables one to underpin the factors that cause a language to change.

Chapter 6 - Probabilistic Phonology: Discrimination and Robustness (by 
Janet B. Pierrehumbert) 

Pierrehumbert discusses a number of studies and supports with evidence
to show that probability can be found at all levels of representation,
first illustrated through ''probability distribution over the phonetic
space'' (p.182). What is more important and is the focus of the
chapter is that speech perception, production and well-formedness is
affected by frequency and is both gradient and predictable. Infants
acquire words first before phonemes and phonemes are gradually
built. In adults, well-formedness judgments for novel words are
affected by frequency, lexical neighbours and phonotactics of existing
words. Finally, Pierrehumbert highlights the fact that phonetic
learning requires continuous updating of probability distribution.

Chapter 7 - Probabilistic Approaches to Morphology (by R. Harald

Baayen's opening pages of his chapter should have actually been at the
beginning of the book, as he encapsulates so nicely how probabilistic
linguistics has come about. This has been due to the development and
the ease of availability of statistical software that can analyse
large amounts of data at a fraction of the time compared to manually
processing. At the same time, technology has enabled to collect and
store large amounts of data, for instance British National Corpus
(BNC), a corpus consisting of 100 million words. With these two
technologies at ones disposal, it is not surprising that we can now
see probabilistic properties in linguistics.

The chapter concentrates on morphological productivity, why people use
certain types of affixes in English and Dutch more than others.
Baayen shows that frequency approaches to measure productivity is not
an appropriate method, as it does not tell you the degree to which
certain affixes are productive. This is illustrated by some simple
English morphological examples -th and -ness using subcorpus of the
British National Corpus. Baayen therefore deals with probabilistic
approaches to determine the factors that aids to the degree of
productivity. The final section of the chapter discusses
morphological segmentation problem, illustrated by computational
models using Matcheck program.

Chapter 8 - Probabilistic Syntax (by Christopher D. Manning) 

Manning highlights that little attention has been devoted to the area
of probabilistic syntax. He therefore examines 'probabilistic models
for explaining language structure' (page 291) because there are a
number of phenomena in syntax where categorical approaches are not
adequate for their explanations. In fact he emphasizes that
probabilistic models should be used in addition to the categorical
approaches to obtain a full understanding of the language structure.

Manning shows throughout his chapter that probabilities can also be
found in syntax, contrary to the statements made by Chomsky and
others. This is demonstrated quickly to the reader, by showing that
the ungrammatical structure 'as least as' (first noted by Manning in
Rosso's book 2001) does not appear to be a typo error or speech error
as first thought. By searching through corpus linguistics, several
instances of these ungrammatical structures were found in the New York
Times newswire and more instances when searched on the web. The
remainder of the first part of chapter looks at verbal clausal
subcategorization frames to which probabilistic syntax models are
applied. In the final section, it gives an overview on Optimality
Theory and Analysis, followed by Stochastic Optimality Theory,
loglinear models and generalized linear models.

CHAPTER 9 - Probabilistic Approaches to Semantics (By Ariel Cohen) 

The final chapter discusses probabilistic techniques in
semantics. Cohen opens the chapter by discussing 'probability', what
do the figures actually mean and tells us when it comes to semantics.
The chapter addresses this issues to generic and frequency adverbs
using ratio theories to show their extensibility.


The uniqueness of this book is that it starts off with an introductory
chapter on probabilistic linguistics and probability theory before
delving deep into probabilistic linguistics. Credit must be given to
the authors for introducing a single book that covers probabilistic
properties that can be found in all areas of linguistics (phonology,
syntax, sociolinguistics, etc.) and showing how traditional
statistical techniques may no longer be appropriate to deal with
complex data analysis work. My only concern is that, although the
book is supposed to be an introductory book on probabilistic
linguistic it is far from that. Some of the chapters in the book
contain complex probabilistic mathematical work which may overwhelm a
linguistic student with limited mathematical experience.

Although I have not been able to provide a detailed account for the
chapters that are not my main focus of research, it has nevertheless
been interesting to read these chapters and to know how probabilistic
techniques can be applied to other fields of linguistic too. I would
therefore advise a linguistic reader to read the book selectively,
start by reading the introduction chapter and probability theory
chapter, which is a must if they have limited background in
probabilistic mathematics, followed by reading the chapters of
interest to their field of research.


Azra Ali is a PhD student in the ARTFORM (Centre for Artificial
Intelligence and Formal Methods) research group in the School of
Computing and Engineering at the University of Huddersfield, England.
Her research area is audiovisual speech errors, phonology, and she is
currently expanding her knowledge in the area of probabilistic
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