|Error Detection with a Maximum Entropy Model|
As mentioned before, we consider error detection as a binary classification task.
Sometimes the step 2) is not necessary if only one effective feature is used (Ueffing and Ney, 2007); and sometimes the step 2) and 3) can be merged into a single step if we directly output predicting results from binary classifiers instead of making thresholding decision.
We integrate two sets of linguistic features into a maximum entropy (MaxEnt) model and develop a MaxEnt-based binary classifier to predict the category (correct or incorrect) for each word in a generated target sentence.
0 We treat error detection as a complete binary classification problem.