N-gram Parsing for Jointly Training a Discriminative Constituency Parser

Syntactic parsers are designed to detect the complete syntactic structure of grammatically correct sentences. In this paper, we introduce the concept of n -gram parsing, which corresponds to generating the constituency parse tree of n consecutive words in a sentence. We create a stand-alone n -gram parser derived from a baseline full discriminative constituency parser and analyze the characteristics of the generated n -gram trees for various values of n . Since the produced n -gram trees are in general smaller and less complex compared to full parse trees, it is likely that n -gram parsers are more robust compared to full parsers. Therefore, we use n -gram parsing to boost the accuracy of a full discriminative constituency parser in a hierarchical joint learning setup. Our results show that the full parser jointly trained with an n -gram parser performs statistically significantly better than our baseline full parser on the English Penn Treebank test corpus.