paper available: Learning the past tense in a recurrent network

[Gary Cottrell]

The following paper will appear in the Proceedings of the
Thirteenth Annual Meeting of the Cognitive Science Society.

It is now available in the neuroprose archive as cottrell.cogsci91.ps.Z.

Learning the past tense in a recurrent network:
Acquiring the mapping from meaning to sounds

Garrison W. Cottrell		Kim Plunkett
Computer Science Dept.		Inst. of Psychology
UCSD				University of Aarhus
La Jolla, CA			Aarhus, Denmark

The performance of a recurrent neural network in mapping a set of plan
vectors, representing verb semantics, to associated sequences of
phonemes, representing the phonological structure of verb morphology,
is investigated. Several semantic representations are explored in
attempt to evaluate the role of verb synonymy and homophony in
deteriming the patterns of error observed in the net's output
performance. The model's performance offers several unexplored
predictions for developmental profiles of young children acquiring
English verb morphology.

To retrieve this from the neuroprose archive type the following:

ftp 128.146.8.62
anonymous
<your netname here>
bi
cd pub/neuroprose
get cottrell.cogsci91.ps.Z
quit

uncompress cottrell.cogsci91.ps.Z
lpr cottrell.cogsci91.ps

Thanks again to Jordan Pollack for this great idea for net distribution.
gary cottrell 619-534-6640 Sec'y: 619-534-5288 FAX: 619-534-7029
Computer Science and Engineering C-014
UCSD, 
La Jolla, Ca. 92093
gary at cs.ucsd.edu (INTERNET)
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gcottrell at ucsd.edu (BITNET)