In order to overcome this problem, <cite>(Poon and Domingos, 2009</cite> ) group parameters and impose local normalization constraints within each group. Given these normalization constraints, and additional structural constraints satisfied by the model, namely that the clauses should be engineered in such a way that they induce treestructured graphs for every sentence, the parameters can be estimated by a variant of the EM algorithm. The class of such restricted MLNs is equivalent to the class of directed graphical models over the same set of random variables corresponding to fragments of syntactic and semantic structure. Given that the above constraints do not directly fit into the MLN methodology, we believe that it is more natural to regard their model as a directed model with an underlying generative story specifying how the semantic structure is generated and how the syntactic parse is drawn for this semantic structure. This view would facilitate understanding what kind of features can easily be integrated into the model, simplify application of non-parametric Bayesian techniques and expedite the use of inference techniques designed specifically for directed models. Our approach makes one step in this direction by proposing a non-parametric version of such generative model.