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Literature-based discovery systems aim at discovering valuable latent connections
between previously disparate research areas. This is achieved by analyzing the
contents of their respective literatures with the help of various intelligent
computational techniques. In this paper, we review the progress of
literature-based discovery research, focusing on understanding their technical
features and evaluating their performance. The present literature-based
discovery techniques can be divided into two general approaches: the traditional
approach and the emerging approach. The traditional approach, which dominate the
current research landscape, comprises mainly of techniques that rely on
utilizing lexical statistics, knowledge-based and visualization methods in order
to address literature-based discovery problems. On the other hand, we have also
observed the births of new trends and unprecedented paradigm shifts among the
recently emerging literature-based discovery approach. These trends are likely
to shape the future trajectory of the next generation literature-based discovery
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