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.

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-

We have replaced KNN by other classifiers, such as those based on Maximum Entropy and Support Vector Machines, respectively.

Similarly, to study the effectiveness of the CRF model, it is replaced by its alternations, such as the HMM labeler and a beam search plus a maximum entropy based classifier.

Other methods, such as classification based on Maximum Entropy models and sequential application of Per-ceptron or Winnow (Collins, 2002), are also practiced.

Maximum entropy (MaxEnt) models1 have been widely used in many NLP tasks (Berger et al., 1996; Ratnaparkhi, 1997; Smith, 2006).

maximum entropy and SVM classifiers) as well as two alternative methods for leveraging unlabeled data (transductive SVMs (Joachims, 1999b) and co-training (Blum and Mitchell, 1998)).

Among the popular semi-supervised methods (e. g. EM on Nai've Bayes (Nigam et al., 2000), co-training (Blum and Mitchell, 1998), transductive SVMs (Joachims, 1999b), and co-regularization (Sindhwani et al., 2005; Amini et al., 2010)), our approach employs the EM algorithm, extending it to the bilingual case based on maximum entropy .