Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

Spring 1991, Vol. 3, No. 1, Pages 110-120
(doi: 10.1162/neco.1991.3.1.110)
© 1991 Massachusetts Institute of Technology
Parsing Complex Sentences with Structured Connectionist Networks
Article PDF (545.54 KB)
Abstract

A modular, recurrent connectionist network is taught to incrementally parse complex sentences. From input presented one word at a time, the network learns to do semantic role assignment, noun phrase attachment, and clause structure recognition, for sentences with both active and passive constructions and center-embedded clauses. The network makes syntactic and semantic predictions at every step. Previous predictions are revised as expectations are confirmed or violated with the arrival of new information. The network induces its own “grammar rules” for dynamically transforming an input sequence of words into a syntactic/semantic interpretation. The network generalizes well and is tolerant of ill-formed inputs.