To validate this conjecture on our translation test data, we compare the reordering performance among the MR08 system, the improved systems and the maximum entropy classifiers.

Then we evaluate the automatic reordering outputs generated from both our translation systems and maximum entropy classifiers.

Potential improvement analysis: Table 7 also shows that our current maximum entropy classifiers have room for improvement, especially for semantic reordering.

Ge (2010) presented a syntax-driven maximum entropy reordering model that predicted the source word translation order.

In order to predict either the leftmost or rightmost reordering type for two adjacent constituents, we use a maximum entropy classifier to estimate the probability of the reordering type rt 6 {M, DM, 8, DS} as follows:

For each pair of constituents, it first extracts its leftmost and rightmost reordering types (line 6) and then gets their respective probabilities returned by the maximum entropy classifiers defined in Section 3.1

Empirically, we show that the proposed measure, based on a maximum entropy classification, satisfied the constraints of the design of an objective measure to a high degree.

5.3.4 Maximum Entropy Model Classifier

We used the maximum entropy classifier implementation in the MaxEnt toolkit4.

One straightforward way of using the maximum entropy classifier’s prediction for our case is to directly use its predicted score-level — l, 2, 3 or 4.

This is done by resorting to a maximum entropy model based approach, to which we turn next.

Par in Column 4 is the number of parameters in the trained maximum entropy model, which indicate the model complexity.

A maximum entropy multi-class classifier is trained and tested on the generated relation instances.

To implement the maximum entropy model, the toolkit provided by Le (2004) is employed.

We apply these approaches in a maximum entropy based system to extract relations from the ACE 2005 corpus.

The TRE systems use techniques such as: Rules (Regulars, Patterns and Propositions) (Miller et al., 1998), Kernel method (Zhang et al., 2006b; Zelenko et al., 2003), Belief network (Roth and Yih, 2002), Linear programming (Roth and Yih, 2007), Maximum entropy (Kambhatla, 2004) or SVM (GuoDong et al., 2005).

The proposed sense-based translation model enables the decoder to select appropriate translations for source words according to the inferred senses for these words using maximum entropy classifiers.

We incorporate these learned word senses as translation evidences into maximum entropy classifiers which form the

Our baseline system is a state-of-the-art SMT system which adapts Bracketing Transduction Grammars (Wu, 1997) to phrasal translation and equips itself with a maximum entropy based reordering model (Xiong et al., 2006).

In order to incorporate word senses into SMT, we propose a sense-based translation model that is built on maximum entropy classifiers.

To predict whether two basic-edits address the same writing problem more discriminatively, we train a Maximum Entropy binary classifier based on features extracted from relevant contexts for the basic edits.

MaXEntMerger We use the Maximum Entropy classifier to predict whether we should merge the two edits, as described in Section 34.

We use a Maximum Entropy classifier along with features suggested by Swanson and Yamangil for this task.