Fusion of two parsers for a natural language processing toolkit

With the rapid growth of real world applications for natural language processing (NLP) systems, there is a genuine demand for a general toolkit from which programmers with no linguistic knowledge can build specific NLP systems. Such a toolkit should have a parser that is general enough to be used across domains, and yet accurate enough for each specific application. In this paper, the fusion of two parsers to achieve both generality and accuracy in handling domain specific NL problems is described. Testing this combined parser on a corpus shows that the accuracy is significantly higher than a system that uses a single parser.