Paper: A Connectionist Architecture for Learning to Parse

ACL ID P98-1087
Title A Connectionist Architecture for Learning to Parse
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998

We present a connectionist architecture and demon- strate that it can learn syntactic parsing from a cor- pus 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 ar- chitectures. We apply these Simple Synchrony Net- works 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.