To resolve conflicts in shift-reduce parsing, we propose a maximum entropy model trained on the derivation graph of training data.

3 A Maximum Entropy Based Shift-Reduce Parsing Model

We propose a maximum entropy model to resolve the conflicts for “h+h”: 2

1. relative frequencies in two directions; 2. lexical weights in two directions; 3. phrase penalty; 4. distance-based reordering model; 5. lexicaized reordering model; 6. n-gram language model model; 7. word penalty; 8. ill-formed structure penalty; 9. dependency language model; 10. maximum entropy parsing model.

Then we propose two novel methods to handle the two PAS ambiguities for SMT accordingly: 1) inside context integration; 2) a novel maximum entropy PAS disambiguation (MEPD) model.

Towards the MEPD model, we design a maximum entropy model for each ambitious source-side PASs.

As to the role ambiguity, we design a novel maximum entropy PAS disambiguation (MEPD) model to combine various context features, such as context words of PAS.

In order to handle the role ambiguities, in this section, we concentrate on utilizing a maximum entropy model to incorporate the context information for PAS disambiguation.

The maximum entropy model is the classical way to handle this problem:

We train a maximum entropy classifier for each Sp via the off-the-shelf MaxEnt toolkit3.

Thus to overcome this problem, we design two novel methods to cope with the PAS ambiguities: inside-context integration and a maximum entropy PAS disambiguation (MEPD) model.

(2010) designed maximum entropy (ME) classifiers to do better rule section for hierarchical phrase-based model and tree-to-string model respectively.

In this work we will use maximum entropy models.

4.1 Maximum entropy models

The maximum entropy framework (Berger et al.

The L-BFGS method (Liu and Nocedal, 1989) was used to estimate the weight parameters of maximum entropy models.

The maximum entropy method with Gaussian prior smoothing was used to estimate the model parameters.

In this work, we use the maximum entropy method (Berger et al., 1996) as a discriminative machine learning method.

The reason for this is that a model based on the maximum entropy method can calculate probabilities.

4.2 Maximum entropy reordering model

(Zens and Ney, 2006) proposed a maximum entropy classifier to predict the orientation of the next phrase given the current phrase.

The classifier can be trained with maximum likelihood like Moses lexicalized reordering (Koehn et al., 2007) and hierarchical lexicalized reordering model (Galley and Manning, 2008) or be trained under maximum entropy framework (Zens and Ney, 2006).

To increase coverage, we train a Maximum Entropy (MaxEnt) classifier (Manning and Klein,

Following previous sequence classification work with Maximum Entropy models (e. g., (Ratna-parkhi, 1996)), we use selected features of adjacent sentences.

We divide both the SR and P&L corpora into training (50%) and test sets (50%) and train a Maximum Entropy (MaxEnt) classifier (Manning and Klein, 2003) with bag-of-word features.

In this paper, we presented a novel structured approach to EC prediction, which utilizes a maximum entropy model with various syntactic features and shows significantly higher accuracy than the state-of-the-art approaches.

We parse our test set with a maximum entropy based statistical parser (Ratna—parkhi, 1997) first.

A maximum entropy model is utilized to predict the tags, but different types of ECs are not distinguished.