Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training .
forest reranking discriminative parsing with nonlocal features
Liang Huang
We conjecture based on our analysis that the EM training algorithm is able to exploit the information available in both gold and automatically labeled data with more complex grammars while being less affected by over-fitting . Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training . Self-training should also benefit other discriminatively trained parsers with latent annotations ( Petrov and Klein , 2008 ) , although training would be much slower compared to using generative models , as in our case .
Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; @@CITATION ) for self training .