| Abstract: | This paper describes a hybrid (symbolic/connectionist) system that performs PP-attachment disambiguation by taking advantage of three distinguishing features of neutral networks: distributed representation, functional compositionality, and inductive learning. The connectionist part of the system follows all the steps performed by the symbolic parser, and drives the parser's behavior by inducing a bias towards the most semantically plausible attachment choices. The sentence to be parsed is read one word at a time. When the symbolic parser has more than one production to apply, the connectionist module has already developed an inner representation of the sentence and a distribution of probabilities over the possible choices. The parser continues its work according to such a distribution. |
