Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion
This work addresses a specific challenge in natural language processing for researchers, but it appears incremental as it builds on existing criteria without demonstrating broad improvements.
The authors tackled the problem of discriminative learning for Probabilistic Context-Free Grammars by proposing a formal framework based on a generalization of the H-criterion, resulting in a new family of objective functions and estimation transformations for parameter estimation.
We present a formal framework for the development of a family of discriminative learning algorithms for Probabilistic Context-Free Grammars (PCFGs) based on a generalization of criterion-H. First of all, we propose the H-criterion as the objective function and the Growth Transformations as the optimization method, which allows us to develop the final expressions for the estimation of the parameters of the PCFGs. And second, we generalize the H-criterion to take into account the set of reference interpretations and the set of competing interpretations, and we propose a new family of objective functions that allow us to develop the expressions of the estimation transformations for PCFGs.