| Error Detection with a Maximum Entropy Model | We tune our model feature weights using an off-the-shelf MaXEnt toolkit (Zhang, 2004). |
| Error Detection with a Maximum Entropy Model | During test, if the probability p(correct|¢) is larger than p(incorrect|¢) according the trained MaXEnt model, the word is labeled as correct otherwise incorrect. |
| Experiments | Starting with MaXEnt models with single linguistic feature or word posterior probability based feature, we incorporated additional features incre-mentally by combining features together. |
| Experiments | We conducted three groups of experiments using the MaXEnt based error detection model with various feature combinations. |
| Experiments | Using discrete word posterior probabilities as features in the MaxEnt based error detection model is marginally better than word posterior probability thresholding in terms of CER, but obtains a 13.79% relative improvement in F measure. |
| Introduction | 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. |