LINGUIST List 8.836

Sat Jun 7 1997

Review: The Balancing Act

Editor for this issue: Andrew Carnie <>

What follows is another discussion note contributed to our Book Discussion Forum. We expect these discussions to be informal and interactive; and the author of the book discussed is cordially invited to join in. If you are interested in leading a book discussion, look for books announced on LINGUIST as "available for discussion." (This means that the publisher has sent us a review copy.) Then contact Andrew Carnie at

Message 1: Klavins et al. "The Balancing Act"

Date: Fri, 6 Jun 1997 11:08:46 -0400 (EDT)
From: Andrew Carnie <>
Subject: Klavins et al. "The Balancing Act"

Klavans J L, Resnik P, (1996)The Balancing Act, Combining Symbolic and
Statistical Approaches to Language, MIT Press, Cambridge Mass., London
England. PP xii and 186. Pbk $17.50

Reviewed by Sam Salt <>

This book brings together a collection of papers first presented at an ACL
workshop in 1994, the purpose of which was "to provide a forum in which to
explore combined symbolic and statistical approaches to natural language."
For anyone with a particular interest in how natural language processing
(NLP) systems can be engineered these papers provide an excellent overview
of some practical approaches to the problem. The difference here is that
not only have the authors made a concerted effort to use both statistical
and symbolic techniques in a hybrid manner but they have also been at pains
to evaluate the costs of such an endeavour. Although the research described
is essentially of a practical nature, the papers also raise some
interesting theoretical questions about the nature of language.

In the 1950s Artificial Intelligence made many extravagant claims regarding
the solution of problems in human cognition. A good example is Artificial
Neural Networks which, it was claimed, would model human thinking
processes. However, both the models used and the computational power
available proved inadequate and Neural Networks languished, not to be
revived until the mid 1980s when sufficiently powerful machines became
As with Neural Networks, so it was with the statistical analysis of
language. Shannon had suggested a number of possible techniques as early as
the late 40's but again lack of computing power ensured that no real
progress was made. Symbolic computational techniques quickly moved in to
fill the gap and gained kudos through association with Chomsky's theories.
It was not until the mid 80's that statistical techniques once more began
to find favour. But by this time the symbolic approach was so well
established that there has been a tendency for the symbolic and statistical
camps to go their separate ways. It would be no exaggeration to say that
the two camps have often been quite hostile towards each other. 
The papers contained in this book try to show how the two techniques can be
mutually complementary and lead to improved analyses. 
To start in the middle of the collection, Vasileios Hatzivassiloglou's
paper gives a good overview of the history of this situation and describes
work which tries to show that using both approaches can pay off in the long
run and actually produces quantifiable improvements. He even tells us that
coding for his system took seven person-months for the statistical
components and five person-months for the linguistic components and goes on
to evaluate the relative contributions of the two components to the system.
Although he recognises that knowledge-based approaches have scaling
problems he nevertheless feels that there is an intuitive belief that the
linguistic models which they often represent are likely to lead to improved
performance when merged with statistical approaches. He is able to
plausibly report, of his finished system, that "many forms of linguistic
knowledge make a significant positive contribution to the performance of
the system." I would recommend his paper as a good starting point for
newcomers as it both outlines the problem and illustrates a plausible route
to solutions. The fact that timescales are discussed comes as a bonus as
it is often difficult to gauge whether work described in papers has taken
days, years or decades to produce results. This is as good an example of
how the two techniques can be combined as you are likely to find. 
Steven Abney's paper on "Statistical Methods and Linguistics" bought home
the fact that linguistics so often deals with competence and not
performance. Computational models often distinguish just between
grammatical and ungrammatical structures but this distinction alone is
insufficient for a real natural language where we might ask whether a
sentence "sounds" right. He advances a system for weighting sentences
according to their apparent degree of sounding natural. Rose and Waibel
also begin with a discussion of the performance versus competence problem
and quite rightly state that it is currently impossible to conceive of a
parser that would deal with all the complexities of performance. They go on
to describe a language-to-language translator which tries to resolve
ambiguities by entering into a dialogue with users. I was mostly
unconvinced by this, as sample transcripts of the dialogue were so lengthy
that I concluded it would be cheaper and quicker to employ a human
translator. In fairness they do recognise this weakness and hope to
simplify matters in future versions of the software. 
Beatrice Daille contributes a useful paper on working with domain-specific
terminology "using shallow syntactic relationships to define the
co-occurrences over which statistical methods operate..", which shows how
collocation can be used to good effect in this field. Patti Price raises
the question of speech recognition and describes how this "traditionally"
has been much more amenable to statistical analysis than the written word
but points out that the success of such approaches depends on the analysis
ignoring the social aspects and effects of language. Ramshaw and Marcus
discuss a corpus-based method using Brill's tagger. 
The papers in this collection are of variable quality, as is so often the
case in such volumes. My notes record that I found at least one of the
papers had odd sentence structure and eccentric punctuation which made it
difficult for me to parse, let alone a machine. Overall though I can
recommend this is a good starting point for anyone prepared to admit that
statistical and symbolic approaches can be usefully combined. I am
convinced that both techniques have contributions to make to the field and
I hope this collection is just the first of many more to come. 

About the Reviewer
 David Salt is Head of the Division of Computing, University of Derby,
England. His specialist interests are Artificial Intelligence,
Consciousness and Computational Linguistics.

Sam Salt
Head of Division of Computing
University of Derby
Kedleston Road
DE22 1GB


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