A Connectionist Architecture for Learning to Parse

We present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. The architecture can represent syntactic constituents, and can learn generalizations over syntactic constituents, thereby addressing the sparse data problems of previous connectionist architectures. We apply these Simple Synchrony Networks to mapping sequences of word tags to parse trees. After training on parsed samples of the Brown Corpus, the networks achieve precision and recall on constituents that approaches that of statistical methods for this task.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: James Henderson, Peter Lane
• Keywords: ACL 1998, ACL 2007, Connectionist Architectures, Syntactic Constituents, Syntactic Parsing