We have developed an automated method that predicts the word accuracy of a speech recognition system for non-native speech, in the context of speaking proficiency scoring. A model...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
This paper proposes a new unsupervised learning method for obtaining English part-ofspecch(POS) disambiguation rules which would improve thc accuracy of a POS tagger. This method ...
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
In this paper, we focus on the challenge of automatically converting a constituency treebank (source treebank) to fit the standard of another constituency treebank (target treeban...