Index of papers in Proc. ACL 2010 that mention
  • MaXEnt
Xiong, Deyi and Zhang, Min and Li, Haizhou
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.
Starting with MaXEnt models with single linguistic feature or word posterior probability based feature, we incorporated additional features incre-mentally by combining features together.
We conducted three groups of experiments using the MaXEnt based error detection model with various feature combinations.
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.
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.
MaXEnt is mentioned in 7 sentences in this paper.
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