Exploring Evolutionary Technical Trends from Academic Research Papers

Automatic Term Recognition (ATR) is concerned with discovering terminology in large volumes of text corpora. 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.

• Type: Conference
• Year: 2008
• Where: DAS
• Authors: Teng-Kai Fan, Chia-Hui Chang
• Keywords: DAS 2008, Document Analysis, Explore Technical Trends, Technical Terms, Technical Trends