Document Categorizer Agent for Computer Science Academic Papers

This paper presents Document Categorizer Agent that categorizes computer science academic papers in .pdf format such as journals and proceedings. In this paper, we propose the use of set of term stored in a database to categorize computer science papers. Few methods and algorithms from related work are considered in improving the categorization process. We have evaluated our document categorizer agent on a number of computer science papers. The categorization process is done by parsing the document, calculating the frequency of each term and matching the terms found with the dataset found in the database. We have shown that the use of this term database can be used to categorize documents. The categorizer agent focuses on categorizing the text document into predetermined category based on the extracted keyword. This can help in making the searching process more efficient and saves the user’s time in searching for the desired document.