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
Table of results
|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, S., Barrett, L., Thibaux, R., and Klein, D. (2006). Learning accurate, compact, and interpretable tree annotation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 433–440, Sydney.
Petrov, S., and Klein, D. (2007). Improved inference for unlexicalized parsing. Proceedings of NAACL 2007, pages 404-411.
- PARSEVAL - the evalb program