Document categorizer agent based on ACM hierarchy

Khalifa Chekima, and Rayner Alfred, and Chin, Kim On and Gan, Kim Soon (2012) Document categorizer agent based on ACM hierarchy. In: Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on.


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As the number of research papers increases, the need for academic categorizer system becomes crucial. This is to help academicians organize their research papers into predefined categories based on the documents’ content similarity. This paper presents the Document Categorizer Agent based on ACM CCS (Association for Computing Machinery Computing Classification System). First, we studied the ACM categories hierarchy. Next, based on these categories, we retrieved our corpus from ACM DL (ACM Digital Library) to train our Categorizer Agent using a popular machine learning technique called Naïve Bayes Classifier. We used two types of training data for the corpus namely, negative training data and positive training data. Next, these papers are categorized according to their content based on the same training data. We tested our Document Categorizer Agent on a number of academic papers to test its accuracy. The result we obtained showed promising results

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Document Categorizer Agent, Agent Technology, Naïve Bayes Classifier, Information Retrieval.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: FACULTY > Faculty of Computing and Informatics
Depositing User: Unnamed user with email
Date Deposited: 17 Nov 2015 07:27
Last Modified: 11 Oct 2017 07:20

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