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 (2004)||Dan Bikel's implementation||?||?|
|Berkeley Parser||Automatically induced PCFG||Petrov et al. (2006), Petrov and Klein (2007)||Berkeley Parser||90.1%||works well also for Chinese and German|
Bikel, D. (2004). On The Parameter Space of Generative Lexicalized Statistical Parsing Models. PhD Thesis, Computer and Information Science, University of Pennsylvania.
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.
Petrov, Barrett, Thibaux and Klein, ACL 2006 
Petrov and Klein, NAACL 2007