Smooth Technologies in Head-Driven Parsing
Lichi Yuan · 2013
Solving the data sparseness problem is an important problem about head-driven parsing,cluster-based statistic language model is an important method to solve the problem of sparse data.Based on the analysis of the classical smoothing technology,this paper proposes a word clustering algorithm by utilizing mutual information and semantic dependency,and an absolute weighted difference method was presented and was used to construct vari-gram language model which has good predictable ability,then proposes an improved head-driven parsing model based on word cluster and vari-gram model.Experiments are conducted for the refined statistical parser,it achieves 84.53% precision and 82.41% recall,F measure is improved 2.02% comparing with the head-driven parsing model introduced by Collins.