Conventional n-best reranking techniques often suffer from the limited scope of the n-best list, which rules out many potentially good alternatives.

We instead propose forest reranking, a method that reranks a packed forest of exponentially many parses.

Our final result, an F—score of 91.7, outperforms both 50-best and 100-best reranking baselines, and is better than any previously reported systems trained on the Treebank.

Discriminative reranking has become a popular technique for many NLP problems, in particular, parsing (Collins, 2000) and machine translation (Shen et al., 2005).

Typically, this method first generates a list of top-n candidates from a baseline system, and then reranks this n-best list with arbitrary features that are not computable or intractable to compute within the baseline system.

conventional reranking only at the root DP-based discrim.

Such a Treebank-style forest is easier to work with for reranking , since many features can be directly expressed in it.

KSDEP 1%CONLL RERANK NO—RERANK BERKELEY STANFORD ENJU ENJU—GENIA

For the other parsers, we input the concatenation of W8] and GENIA for the retraining, while the reranker of RERANK was not retrained due to its cost.

Since the parsers other than NO-RERANK and RERANK require an external POS tagger, a WSJ-trained POS tagger is used with WSJ-trained parsers, and geniatagger (Tsuruoka et al., 2005) is used with GENIA-retrained parsers.

Among these parsers, RERANK performed slightly better than the other parsers, although the difference in the f-score is small, while it requires much higher parsing cost.

retraining yielded only slight improvements for RERANK , BERKELEY, and STANFORD, while larger improvements were observed for MST, KSDEP, NO-RERANK, and ENJU.

RERANK Charniak and Johnson (2005)’s rerank-ing parser.

The reranker of this parser receives n-best4 parse results from NO-RERANK, and selects the most likely result by using a maximum entropy model with manually engineered features.

(2006), who applied a reranked parser to a large unsupervised corpus in order to obtain additional training data for the parser; this self-training appraoch was shown to be quite effective in practice.

However, their approach depends on the usage of a high-quality parse reranker , whereas the method described here simply augments the features of an existing parser.

Note that our two approaches are compatible in that we could also design a reranker and apply self-training techniques on top of the cluster-based features.