dissertation available on connectionist NLP/temporal synchrony

[Jamie Henderson]

FTP-host: linc.cis.upenn.edu
FTP-filename: pub/henderson/thesis.ps.Z
FTP-filename: pub/henderson/chapter1.ps.Z

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/Thesis/henderson.thesis.ps.Z


The following dissertation on the feasibility and implications of
using temporal synchrony variable binding to do syntactic parsing is
available by anonymous ftp or as a technical report.  The complete
dissertation (196 pages) can be ftp'ed from archive.cis.ohio-state.edu
in pub/neuroprose/Thesis/ as henderson.thesis.ps.Z, or from
linc.cis.upenn.edu in pub/henderson/ as thesis.ps.Z.  The first
chapter of this document is a summary (42 pages), and it can be ftp'ed
from linc.cis.upenn.edu in pub/henderson/ as chapter1.ps.Z.  The
complete dissertation is also available as a technical report 
(IRCS Report #94-12) by contacting Jodi Kerper
(jbkerper at central.cis.upenn.edu, (215) 898-0362).  Comments welcome!

					- Jamie Henderson
					  University of Pennsylvania

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	Description Based Parsing in a Connectionist Network

			James Henderson
		  Mitchell Marcus (Supervisor)


Recent developments in connectionist architectures for symbolic computation
have made it possible to investigate parsing in a connectionist network
while still taking advantage of the large body of work on parsing in
symbolic frameworks.  This dissertation investigates syntactic parsing in
the temporal synchrony variable binding model of symbolic computation in a
connectionist network.  This computational architecture solves the basic
problem with previous connectionist architectures, while keeping their
advantages.  However, the architecture does have some limitations, which
impose computational constraints on parsing in this architecture.  This
dissertation argues that, despite these constraints, the architecture is
computationally adequate for syntactic parsing, and that these constraints
make significant linguistic predictions.  To make these arguments, the
nature of the architecture's limitations are first characterized as a set of
constraints on symbolic computation.  This allows the investigation of the
feasibility and implications of parsing in the architecture to be
investigated at the same level of abstraction as virtually all other
investigations of syntactic parsing.  Then a specific parsing model is
developed and implemented in the architecture.  The extensive use of partial
descriptions of phrase structure trees is crucial to the ability of this
model to recover the syntactic structure of sentences within the
constraints.  Finally, this parsing model is tested on those phenomena which
are of particular concern given the constraints, and on an approximately
unbiased sample of sentences to check for unforeseen difficulties.  The
results show that this connectionist architecture is powerful enough for
syntactic parsing.  They also show that some linguistic phenomena are
predicted by the limitations of this architecture.  In particular,
explanations are given for many cases of unacceptable center embedding, and
for several significant constraints on long distance dependencies.  These
results give evidence for the cognitive significance of this computational
architecture and parsing model.  This work also shows how the advantages of
both connectionist and symbolic techniques can be unified in natural
language processing applications.  By analyzing how low level biological and
computational considerations influence higher level processing, this work
has furthered our understanding of the nature of language and how it can be
efficiently and effectively processed.