Following the description in (Lu et al., 2011), we remove neutral sentences and keep only high confident positive and negative sentences as predicted by a maximum entropy classifier trained on the labeled data.

This model use English labeled data and Chinese labeled data to obtain initial parameters for two maximum entropy classifiers (for English documents and Chinese documents), and then conduct EM-iterations to update the parameters to gradually improve the agreement of the two monolingual classifiers on the unlabeled parallel sentences.

(2002) compare the performance of three commonly used machine learning models (Naive Bayes, Maximum Entropy and SVM).

They propose a method of training two classifiers based on maximum entropy formulation to maximize their prediction agreement on the parallel corpus.

After all features are extracted, we use the maXimum entropy toolkit in Section 3.3 to train the maXimum entropy classifier as formulated in Eq.

The essential component of our model is a maXimum entropy classifier pt(e|C that predicts the target translation 6 for a verbal predicate 2} given its surrounding context C(v).

This will increase the number of classes to be predicted by the maximum entropy classifier.

Using these events, we train one maximum entropy classifier per verbal predicate (16,121 verbs in total) via the off-the-shelf MaxEnt toolkit3.

3.2 Maximum Entropy Class-Based N-gram Language Model

The key ideas are the modeling of word n—gram probabilities with a maximum entropy model, and the use of word—class information in the definition of the features.

Both components are themselves maximum entropy n—gram models in which the probability of a word or class label l given history h is determined by %exp(zk fk(h, The features fk(h, l) used are the presence of various patterns in the concatenation of hl, for example whether a particular suffix is present in hl.