abstracts for 2 tech reports in neuroprose

[Risto Miikkulainen]

*********** Do not forward to other bboards *************

The following tech reports are available by anonymous ftp from the
pub/neuroprose directory at cheops.cis.ohio-state.edu:

A DISTRIBUTED FEATURE MAP MODEL OF THE LEXICON
Risto Miikkulainen
Ailab, Computer Science Department, UCLA
Technical Report UCLA-AI-90-04

DISLEX models the human lexical system at the level of physical
structures, i.e. maps and pathways. It consists of a semantic memory and
a number of modality-specific symbol memories, implemented as feature
maps.  Distributed representations for the word symbols and their
meanings are stored on the maps, and linked with associative
connections. The memory organization and the associations are formed in
an unsupervised process, based on co-occurrence of the physical symbol
and its meaning.  DISLEX models processing of ambiguous words, i.e.
homonyms and synonyms, and dyslexic errors in input and in production.
Lesioning the system produces lexical deficits similar to human aphasia.
DISLEX-1 is an AI implementation of the model, which can be used as the
lexicon module in distributed natural language processing systems.

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A NEURAL NETWORK MODEL OF SCRIPT PROCESSING AND MEMORY
Risto Miikkulainen
Ailab, Computer Science Department, UCLA
Technical Report UCLA-AI-90-03

DISCERN is a large-scale NLP system built from distributed neural
networks. It reads short narratives about stereotypical event sequences,
stores them in episodic memory, generates fully expanded paraphrases of
the narratives, and answers questions about them. Processing is based on
hierarchically organized backpropagation modules, communicating through
a central lexicon of word representations. The lexicon is a double
feature map, which transforms the physical word symbol into its semantic
representation and vice versa. The episodic memory is a hierarchy of
feature maps, where memories are stored ``one-shot'' at different
locations.  Several high-level phenomena emerge automatically from the
special properties of distributed neural networks.  DISCERN plausibly
infers unmentioned events and unspecified role fillers, and exhibits
plausible lexical access errors and memory interference behavior. Word
semantics, memory organization and the appropriate script inferences are
extracted from examples.

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To obtain copies, either:

a) use the getps script (by Tony Plate and Jordan Pollack, posted on
connectionists a few weeks ago)

b)
unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): <ret>
ftp> cd pub/neuroprose
ftp> type binary
ftp> get
(remote-file) miikkulainen.lexicon.ps.Z
(local-file) foo.ps.Z
ftp> get
(remote-file) miikkulainen.discern.ps.Z
(local-file) bar.ps.Z
ftp> quit
unix> uncompress foo.ps bar.ps
unix> lpr -P(your_local_postscript_printer) foo.ps bar.ps