Journal of Educational Media and Library Sciences

Abstract

Because the size of modern scientific research is larger than before and the task of research becomes even more complex, researchers and managers urgently need an effective method to explore important topics in research domains. In the past, we had proposed a series of technologies based on text processing and text mining to deal with such a problem. Using text information in papers of the examined domain as input, a technology for term extraction was proposed to select key terms in the text information to represent important topics in the domain. Another proposed technology for information visualization was used to present the terms and their relationships in two-dimensional graphs with a technology of information visualization. Users can easily browse the topics of the domain as well as their development through the generated graphs for decision making of research and management. In addition, the technologies include several techniques of estimating term co-occurrences, calculating degrees of relevance between topics, and mapping paper information to the topics graph. In this paper, an automatic method for topic exploration was proposed with the integration of the developed technologies and it was applied to the studies of computational linguistics in Taiwan to depict foci of research and development in the domain. The result shows that for the development of technologies of machine translation, the earlier studies in the domain emphasized the computational theorization of several linguistic knowledge, but in its mid and later periods, there were more applications emerging, such as speech processing and information retrieval, and a lot of statistical approaches were adopted as the technologies for their robustness and easy implementation.

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