Exploring evolutionary technical trends from academic research papers

Teng Kai Fan, Chia Hui Chang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

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主出版物標題DAS 2008 - Proceedings of the 8th IAPR International Workshop on Document Analysis Systems
頁面574-581
頁數8
DOIs
出版狀態已出版 - 2008
事件8th IAPR International Workshop on Document Analysis Systems, DAS 2008 - Nara, Japan
持續時間: 16 9月 200819 9月 2008

出版系列

名字DAS 2008 - Proceedings of the 8th IAPR International Workshop on Document Analysis Systems

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???event.eventtypes.event.conference???8th IAPR International Workshop on Document Analysis Systems, DAS 2008
國家/地區Japan
城市Nara
期間16/09/0819/09/08

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