Paper: Corpus Variation And Parser Performance

ACL ID W01-0521
Title Corpus Variation And Parser Performance
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2001
Authors
  • Daniel Gildea (University of California at Berkeley, Berkeley CA)

Most work in statistical parsing has focused on a single corpus: the Wall Street Journal portion of the Penn Treebank. While this has allowed for quanti- tative comparison of parsing techniques, it has left open the question of how other types of text might a#0Bect parser performance, and how portable pars- ing models are across corpora. We examine these questions by comparing results for the Brown and WSJ corpora, and also consider which parts of the parser's probability model are particularly tuned to the corpus on which it was trained. This leads us to a technique for pruning parameters to reduce the size of the parsing model.