Parsing (State of the art)
- Performance measure: PARSEVAL - the evalb program
- Training data: sections 2-22 of Wall Street Journal corpus
- Testing data: section 23 of Wall Street Journal corpus
|System name||Short description||Main publications||Software||Results (PARSEVAL)||Comments|
|Johnson & Charniak's Parser||Lexicalized N-Best PCFG + Discriminative re-reanking||Johnson and Charniak (2005)||download||91.4%||works well also on Brown|
|Collins' Parser||Lexicalized PCFG||Collins (1999), Bikel's Thesis ||Dan Bikel's implementation||???|
|Berkeley Parser||Automatically induced PCFG||Petrov, Barrett, Thibaux and Klein, ACL 2006 , Petrov and Klein, NAACL 2007 ||Berkeley Parser||90.1%||works well also for Chinese and German|
Collins, M. (1999). Head-driven Statistical Models for Natural Language Parsing. PhD Thesis, Computer and Information Science, University of Pennsylvania.
Johnson, M., and Charniak, E. (2005). Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. Proceedings of the 43rd Annual Meeting of the ACL, pages 173–180, Ann Arbor, June 2005.