Paper available in Neuroprose

[Prahlad.Gupta@K.GP.CS.CMU.EDU]

The following paper has been placed in the Neuroprose archive, as the
file gupta.stress.ps.Z

Comments are invited.

Retrieval instructions follow the abstract below.  Thanks to Jordan
Pollack for making this facility available.

-- Prahlad
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           CONNECTIONIST MODELS & LINGUISTIC THEORY:
         INVESTIGATIONS OF STRESS SYSTEMS IN LANGUAGE

         PRAHLAD GUPTA                DAVID S. TOURETZKY
   --------------------------      --------------------------
      Dept. of Psychology          School of Computer Science
   Carnegie Mellon University      Carnegie Mellon University
      Pittsburgh, PA 15213           Pittsburgh, PA 15213
       prahlad at cs.cmu.edu                dst at cs.cmu.edu
   ============================================================



                             Abstract
                             --------

This work describes the use of connectionist techniques to model the
learning and assignment of linguistic stress.  Our aim was to explore
the ability of a simple perceptron to model the assignment of stress
in individual words, and to consider, in light of this study, the
relationship between the connectionist and theoretical linguistics
approaches to investigating language.

We first point out some interesting parallels between aspects of the
model and the constructs and predictions of Metrical Phonology, the
linguistic theory of stress: (1) the distribution of learning times
obtained from perceptron experiments corresponds with theoretical
predictions of "markedness," and (2) the weight patterns developed
by perceptron learning bear a suggestive *structural* relationship to
features of the linguistic analysis, particularly with regard to
"iteration" and "metrical feet".

We use the connectionist learning data to develop an analysis of
linguistic stress based on perceptron-learnability.  We develop a
novel characterization of stress systems in terms of six parameters.
These provide both a partial description of the stress pattern itself
and a prediction of its learnability, without invoking abstract
theoretical constructs such as "metrical feet."  Our parameters
encode linguistically salient concepts as well as concepts that have
computational significance.

These two sets of results suggest that simple connectionist learning
techniques have the potential to complement, and provide computational
validation for, abstract theoretical investigations of linguistic
domains.

We then examine why such methodologies should be of interest for
linguistic theorizing.  Our analysis began at a high level by
observing inherent characteristics of various stress systems, much as
theoretical linguistics does.  However, our explanations changed
substantially when we included a detailed account of the model's
processing mechanisms.  Our higher-level, theoretical account of
stress was revealed as only an *approximation* to the lower-level
computational account.  Without the ability to open up the black boxes
of the human processor, linguistic analyses are arguably analogous to
our higher-level descriptions.  This highlights the need for
*computational grounding* of theory-building.  In addition, we suggest
that there are methodological problems underlying parameter-based
approaches to learnability.  These problems make it all the more
important to seek sources of converging evidence such as is provided
by computational models.
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To retrieve the paper by anonymous ftp:

        unix> ftp archive.cis.ohio-state.edu  # (128.146.8.52)
        Name: anonymous
        Password: neuron
        ftp> cd pub/neuroprose
        ftp> binary
        ftp> get gupta.stress.ps.Z
        ftp> quit
        unix> uncompress gupta.stress.ps.Z
        unix> lpr -P<printer name> gupta.stress.ps
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