Maximum Entropy Modeling in Sparse Semantic Tagging

In this work, we are concerned with a coarse grained semantic analysis over sparse data, which labels all nouns with a set of semantic categories. To get the benefit of unlabeled data, we propose a bootstrapping framework with Maximum Entropy modeling (MaxEnt) as the statistical learning component. During the iterative tagging process, unlabeled data is used not only for better statistical estimation, but also as a medium to integrate non-statistical knowledge into the model training. Two main issues are discussed in this paper. First, Association Rule principles are suggested to guide MaxEnt feature selections. Second, to guarantee the convergence of the bootstrapping process, three adjusting strategies are proposed to soft tag unlabeled data.