(2006), and 3.4% over a nonlocal constituent reranker .

Table 3: Parsing results for reranking 50-best lists of Berkeley parser (Dev is WSJ section 22 and Test is WSJ section 23, all lengths).

For constituent parsing, we rerank the output of the Berkeley parser (Petrov et al., 2006).

For constituent parsing, we use a reranking framework (Charniak and Johnson, 2005; Collins and Koo, 2005; Collins, 2000) and show 9.2% relative error reduction over the Berkeley parser baseline.

We then add them to a constituent parser in a reranking approach.

We also verify that our features contribute on top of standard reranking features.3

Because the underlying parser does not factor along lexical attachments, we instead adopt the discriminative reranking framework, where we generate the top-k candidates from the baseline system and then rerank this k-best list using (generally nonlocal) features.

We take a maximum entropy reranking approach to the problem which admits arbitrary features on a permutation of modifiers, exploiting hundreds of thousands of features in total.

The straightforward maximum entropy reranking approach is able to significantly outperform preVious computational approaches by allowing for a richer model of the prenominal modifier ordering process.

By mapping a set of features across the training data and using a maximum entropy reranking model, we can learn optimal weights for these features and then order each set of modifiers in the test data according to our features and the learned weights.

In Section 3 we present the details of our maximum entropy reranking approach.

We treat the problem of prenominal modifier ordering as a reranking problem.

At test time, we choose an ordering cc 6 7r(B) using a maximum entropy reranking approach (Collins and Koo, 2005).

In this next section, we describe our maximum entropy reranking approach that tries to develop a more comprehensive model of the modifier ordering process to avoid the sparsity issues that previous ap-