Charniak and Johnson 2005

This paper is a work in progress by Francis Keith

Citation

"Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking", Eugene Charniak, Mark Johnson, ACL 2005

Online Version

An online version of this paper can be found here: [1]

Summary

The authors describe a method that uses discriminative reranking for parsing. It uses the standard Charniak parser to generate the 50 best parse trees for a given sentence. They use the 50 parse trees in a MaxEnt reranker. The results show a small improvement over the state of the art (at the time), but is a significantly more efficient.

Getting the $n$-best parses

They first describe using a simple extension to the dynamic programming algorithm to find the best parse (i.e. Viterbi), and instead store the $n$ best parses. The problem with this approach is that it increases the storage space significantly, especially if states are split a lot.

However, the parser they used solves the space problem by using a "coarse-to-fine" approach. They do this by using a "crude" grammar that only contains CFG features and parent and neighbor labels, which contains few enough states to be able to use a simple bottom up dynamic programming approach. Because of the coarseness of this parse, they get a polynomial sized forest. Each edge is pruned based on marginal probabilities conditioned on the string $s$, if the probability given by the following equation falls below a threshold (they use $10^{-4}$)

$p(n_{j,k}^{i}|s)={\frac {\alpha (n_{j,k}^{i})\beta (n_{j,k}^{i})}{p(s)}}$

The fine-grain model is then applied, but due to the smaller candidate set, the space size is manageable. During evaluation, the $n$-best paths are kept at each state.

They produce results on section 23 of the WSJ for various numbers of best parses.

n	1	2	10	25	50
$f$-score	0.897	0.914	0.948	0.960	0.968