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Journal of Information Science and Engineering, Vol. 26 No. 1, pp. 97-117 (January 2010)

Exploring Evolutionary Technical Trends From Academic Research Papers*

TENG-KAI FAN AND CHIA-HUI CHANG
Department of Computer Science and Information Engineering
National Central University
Chungli, 320 Taiwan

Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics.

Keywords: term classification, supervised machine learning, text mining, information extraction, trends analysis

Full Text () Retrieve PDF document (201001_08.pdf)

Received March 3, 2008; revised July 14, 2008; accepted September 18, 2008.
Communicated by Jorng-Tzong Horng.
* The work was supported in part by the National Science Council of Taiwan, No. NSC 96-2627-E-008-001.
1 http://www.nist.gov/tac/.