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

Yuen-Hsien Tseng, Yi Yang Lee, Te Yi Chan

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish
Pages189-193
Number of pages5
Publication statusPublished - 2009 Jan 1
Event12th International Conference on Scientometrics and Informetrics, ISSI 2009 - Rio de Janeiro, Brazil
Duration: 2009 Jul 142009 Jul 17

Other

Other12th International Conference on Scientometrics and Informetrics, ISSI 2009
CountryBrazil
CityRio de Janeiro
Period09/7/1409/7/17

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ASJC Scopus subject areas

  • Information Systems

Cite this

Tseng, Y-H., Lee, Y. Y., & Chan, T. Y. (2009). Term mining for relation visualization and exploration: The case of agricultural news reports in Taiwan. 189-193. Paper presented at 12th International Conference on Scientometrics and Informetrics, ISSI 2009, Rio de Janeiro, Brazil.