Dear All:
I tried to post the message clipped below at around 2:30 p.m. today, but
as far as I can tell, it didn't make it - It doesn't show up on my list
and I never got a receipt note. This may simply be a case of the
internet exercising aesthetic judgment, but in case it's accidental, I'm
trying again.
One addition - I'd like to make it clear that what I'm kvetching about
are three specific *characteristics* of some model-theoretic frameworks.
Many frameworks don't have full determinacy or atemporal metrics - but a
lot of the ones in linguistics do, and those are the ones I'm talking
about.
__
Bruce,
Science does not, in fact, depend on that kind of model. Quite a number
of scientific theories are couched in terms of such models, but that's
not why they're scientific - rather, it's because they make predictions
that can be falsified by reference to mutually observable phenomena.
Conflating that kind of model with science itself is precisely the kind
of thing I was whinging about.
We don't even have to go far afield to find scientific theories (or at
least, theories as, or more widely recognized as such, than those in
linguistics) that don't use models with the three characteristics I was
discussing. Biology, by and large, doesn't use atemporal simplicity
metrics. The operation of a system, in real time, to perform real tasks,
is an integral part of biological models. If a system with fewer rules
and fewer primitives nevertheless requires far more steps to achieve a
particular goal than another system, its inefficiency counts for at
least as much against it as its simplicity (in terms of an atemporal
metric) counts for it.
I would also argue that neural networks do not "use" objects in the
sense that a standard grammatical model uses a symbol like "noun,"
although I did not make enough of a distinction in my original post.
You're certainly right that people talk about neural networks in terms
of objects (nodes) and their properties (activation thresholds, etc.).
However, they're not talking about language when they do that. The nodes
and thresholds are not within the domain itself that the network is
modeling. In an "objects and rules" model, the objects are themselves
part of the "content" the system is modeling; for example, "noun," or
a value like "+N," in a standard grammatical model is considered to be
part of language itself. In a neural network model of language,
"threshold value" is not considered to be a linguistic phenomenon being
modeled, but rather part of the description of the system by which a
linguistic phenomenon is modeled. So, you're entirely right that you can
consider neural networks to have objects, but those are transparently
presented as features of the model not features of language. Saying
something about a threshold value is saying something about the device
language is conjectured to be running on, not saying something about
language. While the network can be described in terms of objects and
rules, the network itself is not manipulating objects when it operates.
There's no sense in which a neural network model of language has to
presuppose objects as part of language.
Bill Spruiell
Dept. of English
Central Michigan University.
From: Assembly for the Teaching of English Grammar
[mailto:[log in to unmask]] On Behalf Of Bruce Despain
Sent: Wednesday, October 17, 2007 5:18 PM
To: [log in to unmask]
Subject: Re: Rules ad nauseam
Bill,
Thanks for the notice, though I was stultified (proved to be of unsound
mind?).
The set of symbols used as primitives and a set of rules, laws, or
principles, is not unique to linguists. In fact science itself consists
of this kind of model. To attack such a model is to attack science,
which has a much longer track record than any linguist or English maven.
The objects to be manipulated by language are sounds. (disputable?)
The objects to be manipulated by language are meanings. (disputable?)
The objects to be manipulated by language are signs written down in
symbols. (disputable?)
The objects to be manipulated by language are combinations of the above.
(disputable?)
1) What seems to be the nature of neural nets doesn't mean that no
objects don't exist in the model. Indisputable is that fact that there
are potentials, thresholds, charges, etc. involved. The model-theoretic
framework says nothing about the reality of the objects posited. The
model is no more than a metaphor for what is being modeled. 2) A fully
determinate system does not come to mind when the modeling is of
phenomena like the weather. There are simply too many elements in the
determinate version. Even the particle theory of matter must be
abandoned though it is clearly determinate. The sheer number of
particles involved often becomes so great as to make the model
impractical in making predictions of any but the roughest statistical
kind. Certainly the determinate nature of the phenomena described ought
to be paralleled by that of the model that describes it. The
investigator who wants to put the former in doubt, has no need for a
model with full predictive power. 3) The number of objects and rules
has been a condition since the acceptance of Occam's razor by most
scientists. I would think that the simple nature of these objects and
the statement of these rules in the accepted vernacular of mathematics
would be another consideration.
I think that there is a likely confusion between a mathematical model
and a particular model for linguistic descriptions. The model-theoretic
principles of mathematics are unavoidable to any formalization. Whether
a specific mathematical model actually serves to describe the phenomena
it claims to can always be disputed and refuted with appropriate data.
But we don't thow away the language because of what people can say with
it. If the phenomena we are trying to describe are indeterminate, then
it doesn't make much sense to use a model that requires determinate
phenomena. But the model itself had better use a determinate framework,
or it would hardly be able to explain anything (have any predictive
power).
I would wonder that failures in the operation (?) of the model of
language could ever be made out to be performance issues. The
performance of the model would have to be faultless, in order to support
itself. Maybe people expect it to make predictions of the performance
variety? (Present-day minimalists are often far too broad in their
expectations in this area.) You say, "The problem is not the framework
itself, but rather its hegemonic status." I think that what many
linguists call the model-theoretic framework is their own model for
natural language. This is the area of hegemony, I believe. This then
pits one camp against another based on their own modeling principles
established for their own purposes, not the two elements you gave for a
mathematical model in science. Certain models can in fact be shown to
be faulty based on the way in which the nature of the elements and
mathematical relationships posited do not match in principle the
behavior of the objects being modeled. To do so does require a bit of
sophistication, which, sorry to say, I do not possess. (Cf. John Casti,
Reality Rules)
Bruce
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