Mühendislik alanındaki Türkçe akademik metinler için makine öğrenmesi destekli doğal dil işleme çalışmaları ve bir karar destek sisteminin geliştirilmesi: TÜBİTAK projeleri örneği

The information retrieved from the academic texts such as articles, proceedings, thesis and project proposals are used for a wide range of purposes. In the first phase of this study; a library, that can transform the raw text into a standard form, is created by considering the key terms/features in the engineering field. Then, the key terms that can best represent the document are retrieved and a similarity detection algorithm is developed using these terms. Finally, the Naïve Bayes Classifier in machine learning is used to assign the documents to the appropriate engineering sub-fields. The project proposals submitted to TUBITAK Academic Research Funding Program Directorate (ARDEB) are analyzed as a case study. The results indicate that the proposed similarity algorithm correctly detects almost all of the revised proposals while the accuracy of the classifier is 83.3% in the first prediction and reaches up to 96.4% in the first three predictions over a sample of 1255 proposals. [ABSTRACT FROM AUTHOR]