We propose a general model for joint inference in correlated natural language processing tasks when fully annotated training data is not available, and apply this model to the dual tasks of word sense disambiguation and verb subcategorization frame determination. The model uses the EM algorithm to simultaneously complete partially annotated training sets and learn a generative probabilistic model over multiple annotations. When applied to the word sense and verb subcategorization frame determination tasks, the model learns sharp joint probability distributions which correspond to linguistic intuitions about the correlations of the variables. Use of the joint model leads to error reductions over competitive independent models on these tasks.
REF propose a method to learn a joint generative inference model from partially labeled data and apply their method to the problems of word sense disambiguation for verbs and determination of verb subcategorization frames.
Verb Sense And Subcategorization: Using Joint Inference To Improve Performance On Complementary Tasks
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science