A Connectionist Parser Aimed at Spoken Language

Ajay Jain, Alex Waibel


Abstract
We describe a connectionist model which learns to parse single sentences from sequential word input. A parse in the connectionist network contains information about role assignment, prepositional attachment, relative clause structure, and subordinate clause structure. The trained network displays several interesting types of behavior. These include predictive ability, tolerance to certain corruptions of input word sequences, and some generalization capability. We report on experiments in which a small number of sentence types have been successfully learned by a network. Work is in progress on a larger database. Application of this type of connectionist model to the area of spoken language processing is discussed.
Anthology ID:
W89-0224
Volume:
Proceedings of the First International Workshop on Parsing Technologies
Month:
August
Year:
1989
Address:
Pittsburgh, Pennsylvania, USA
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Carnegy Mellon University
Note:
Pages:
221–229
Language:
URL:
https://www.aclweb.org/anthology/W89-0224
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W89-0224.pdf