Exploring evolutionary technical trends from academic research papers

Teng Kai Fan, Chia Hui Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationDAS 2008 - Proceedings of the 8th IAPR International Workshop on Document Analysis Systems
Pages574-581
Number of pages8
DOIs
StatePublished - 2008
Event8th IAPR International Workshop on Document Analysis Systems, DAS 2008 - Nara, Japan
Duration: 16 Sep 200819 Sep 2008

Publication series

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

Conference

Conference8th IAPR International Workshop on Document Analysis Systems, DAS 2008
Country/TerritoryJapan
CityNara
Period16/09/0819/09/08

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