Research On Method And Application Of Text Mining In The Field Of Scientific Research Project Management

Similarity analysis of projects is a basic management problem in the domain of scientific research project management of fundamental research. On the basis of similarity, projects can be classified to avoid repetition and appropriate experts can be selected to evaluate projects. Similarity of projects is analyzed by manager based on experience, title, abstract, and keywords of scientific research project requisitions. The main characteristics of fundamental research are innovation, uncertainty, fusion and cross of subjects, continuous appearance of new viewpoint and new concept. With the rapid increase of projects, it's difficult for the project manager to analyze the similarity on the basis of the project's meaning. This is a great challenge to the project management, so similarity analysis from the knowledge meaning of project is a practical requirement.Discovering knowledge from projects and discussing the problem of scientific research management from the point of knowledge management is really a problem.The scientific research project requisitions are texts written by natural language and most requisitions of fundamental research in China are Chinese texts. So knowledge discovery from projects is text mining from requisitions. The basic methods of text mining are studied on the basis of the characteristics of scientific research project requisitions of fundamental research. The main research work of this paper is listed below:1. A new segmentation idea which is on the basis of longer strings first and need not dictionary is proposed. Compared with English, segmentation is a basic problem of Chinese text mining. Plentiful professional terminologies which have semantic integrity exist in scientific research project text and new domain-specific terms increase continuously, especially in Chinese text of fundamental research. Current segmentation methods do not suit text of fundamental research, so an idea of longer strings first and without using dictionary is put forward in this paper.2. Chinese scientific research project text's segmentation methods are proposed. Three text segmentation methods without using dictionary are proposed based on above idea: maximum matching and frequency statistics (MMFS), reverse maximum matching and frequency statistics (RMMFS), bidirectional maximum matching and frequency statistics (BMMFS). The segmentation results indicate that BMMFS has better precision. Combining statistics and rules, these methods can get special semantic strings, phrases and words. The...