Exploiting reference section to classify paper's topics

Classification is an important task in data mining. Classification is about organizing data into relevant nodes in taxonomy. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as: information retrieval, finding experts, recommender systems etc. In Computer Science, the ACM categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In the past, different approaches have been applied to find the actual topics of a paper such as content based analysis, metadata analysis, and semantic analysis etc. However, in this paper, we exploit the reference section of a research paper to discover topics of the paper. It is assumed that in most of the cases, papers belonging to the same or similar category are cited by an author. We have evaluated our technique for a dataset of Journal of Universal Computer Science (J.UCS). Our system collected 1460 documents from J. UCS along with their predefined topics assigned by authors and verified by journal's administration. The system used 1010 documents for training dataset. The system extracted references from training dataset and grouped them in a Topic Reference pair such as TR {Topic, Reference}. Subsequently, the system was tested on the remaining 450 documents. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The system collects corresponding list of topics matched with the references in the said pair. Subsequently multiple weights are assigned during the process of this matching. The system was able to predict the first node in the ACM topic (topic A to K) with 70% accuracy.