Research on Novel Methodology for Documentation Taxonomy for English IDIOLECT to GAIN Lesser Ambiguity Results

Information in the form of textual documents would have a huge growth due to the factors like digitization of libraries, exponential rise in internet usage, use of e-mail for various reasons, acceptability of soft copies for many works even critical financial work and other things. In view of these reasons, this paper discusses a new approach for Documentation Taxonomy (DT) based on combining efficient algorithms to be used for english language. The important aspect of automatically classifying and providing taxonomy to a set of documents into any taxonomical structure with the help of predefined categories is termed as Documentation Taxonomy. Automated Documentation Taxonomy is gaining prominence since it frees organizations from the frantic and time consuming need of manually organizing documents. This manual process can be expensive and given the time constraints of the application or the number of documents involved, simply not feasible. In terms of accuracy, modern documentation taxonomy systems proves better than that of trained human professionals, which is made possible by a combination of information retrieval technology and machine learning technology. This research put forwards a mechanism that shows that the use of combination of different algorithms brings down the ambiguity in taxonomical issues. There are numerable useful applications of this approach spanning various scientific and general fields of work.