Term mining for relation visualization and exploration: The case of agricultural news reports in Taiwan

An efficient term mining method to build a general term network is presented for term relation visualization and exploration. Terms from each document in the corpus are first identified. They are subject to an analysis for their association weights, which are accumulated over all the documents. The resulting term association matrix is used to build a general term network. A set of terms having similar attributes can then be given to extract the desired sub-network from the general term network for visualization. This analysis scenario based on the collective terms of the same type enables evidence-based relation exploration. Our application examples show that term relations, be it causality, coupling, or others, can be effectively revealed and verified by the underlying corpus. This work contributes by presenting an efficient and effective term-relationship mining method and extending the applicability of term networks to a broader range of informetric tasks.