Title :
Exploring Evolutionary Technical Trends from Academic Research Papers
Author :
Fan, Teng-Kai ; Chang, Chia-Hui
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Jhongli
Abstract :
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.
Keywords :
data mining; document image processing; learning (artificial intelligence); object recognition; text analysis; ACM; academic research papers; automatic term recognition; evolutionary technical trends; machine learning algorithms; text corpora; Data mining; Frequency; Information analysis; Information retrieval; Paper technology; Search engines; Terminology; Text analysis; Text mining; Text recognition; Automatic Term Recognition; Supervised Machine Learning; Text Mining;
Conference_Titel :
Document Analysis Systems, 2008. DAS '08. The Eighth IAPR International Workshop on
Conference_Location :
Nara
Print_ISBN :
978-0-7695-3337-7
DOI :
10.1109/DAS.2008.25
