Statistical language paring model based on dependency
Lichi Yuan · Journal of Central South University(Science and Technology) · 2009
By incorporating linguistic features such as semantic dependency and syntactic relations, a novel statistical Parsing model was proposed. The experiments were conducted for the refined statistical parser. The results show that the model is constructed on word cluster, so the problem of data sparseness is not serious. The model can take advantage of a few semantic dependencies at the same time. The model is a parser based on lexicalized model, it is combined with segmentation and POS tagging model and thus a language parser is built. The questions caused by context-free hypothesis and ancestor-free hypothesis in probability context free grammar are solved well in this model. It achieves 86.96% precision and recall 85.25%, F value is improved by 4.75% compared with that of the head-driven parsing model introduced by Collins.