A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is
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 precisiondoi:10.3115/980845.980934 dblp:conf/acl/HendersonL98 fatcat:6xo6jw5morhmhi4upfd3ymkgoq