An unsupervised cascade learning scheme for ‘cluster-theme keywords’ structure extraction from scientific papers.

The large amount of scientific papers provides a convenient way for users to know the latest research progress of a specific research topic. However, the large volume and the diverse research themes hiding among these papers usually hinder users from conveniently locating the specific papers that they are interested in. To tackle this problem, we propose a novel unsupervised cascade learning scheme that aims to extract a ‘cluster-theme keywords’ structure from the related papers of a research topic so as to help users locate their research interests quickly. Our approach first selects some representative papers for a research topic. It then clusters these selected papers into several small clusters with the help of a domain ontology. It finally extracts some theme keywords for each cluster. Our approach not only greatly reduces the time-consuming and labour-intensive paper-seeking process for users, but also comprehensively displays the diverse themes of a research topic. We conducted extensive experiments to evaluate our proposed approach. The experimental results demonstrate the effectiveness of this approach, which produces promising results. [ABSTRACT FROM PUBLISHER]