Hierarchically Classified Probabilistic Grammar Parsing
层级分类概率句法分析

This paper analyzed various existing approaches of structural grammar parsing, and addressed the problem of over-classification and under-classification. Then a hierarchically classified phase structure grammar (HC-PSG) and a hierarchically classified probabilistic context-free grammar (HC-PCFG) parsing are proposed to respond to this challenge. A measure of class clustering is designed to eliminate the classification ambiguity of grammar rules. The HC approach implements a general learning rule from a small number of phrase instances. An instant clustering method is used to disambiguate rules learned from corpus. The HC method is also extended to context sensitive grammar parsing to improve performance. It employs the classification of the context relevancy to handle the problem of corpus sparsity. By all the means, it can leverage the conflicts between under-classification and over-classification.