Natural language processing technologies offer ease-of-use of computers for average users, and ease-of-access to on-line information. Natural language, however, is complex, and the traditional methods of parsing with a single grammar and parser may result in an inefficient and large system that is difficult to maintain and is fragile in dealing with language irregularities. This paper begins by reviewing an alternative effort in grammar decomposition (also known as grammar partitioning) for natural language parsing, which aims to alleviate these problems. We then propose a novel automatic approach for grammar partitioning, in comparison with a random method of partitioning. Our experiments show that syntactic GLR parsing is formidable for the Wall Street Journal corpus in the Penn Treebank when a single grammar is used. This is due to too many grammar rules for parsing table generation. However, grammar partitioning solves the problem and offers a viable alternative. Our results also show that our automatic grammar partitioning method based on the mutual information criterion fares better than a random partitioning method and exhibits efficiency in parsing as well as high parse coverage. 1