Cyber Times International Journal of Technology & Management

Text information scenario is expected to witness an exponential growth due to several factors like digitization of libraries, growth of Internet usage, communication via e-mail, acceptability of soft copies and other things. This paper discusses a new approach for Documentation Taxonomy (DT) based on combining efficient algorithms. The important aspect of automatically sorting and classifying a set of documents into any category by incorporating a predefined set is Documentation Taxonomy. Automated Documentation Taxonomy is gaining notability since it frees organizations from the hectic and time consuming need of manually organizing documents, which can be too expensive, or simply not feasible given the time constraints of the application or the number of documents involved. 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 in Documentation Taxonomy approach. There are numerable useful applications of this approach spanning various scientific and general fields of work. Also the approaches of standard input and tokenization are considered for a better output which shall be devoid of any complexity for Documentation Taxonomy.