Journal article topic detection based on semantic features

Hei Chia Wang, Tian Hsiang Huang, Jiunn Liang Guo, Shu Chuan Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal's archives. However, the users still have to deal with a huge number of search results. How to provide an efficient search, i.e., to present the search results in categories, has become an important current research issue. If search results can be classified and shown by their topics, users can find papers of interest quickly. However, traditional topic detection methods use only word frequencies, ignoring the importance of semantics. In addition, the bibliographic structures (e.g., Title, Keyword, and Abstract) have particular importance. Therefore, this paper describes a topic detection method based on bibliographic structures and semantic properties to extract important words and cluster the scholarly literature. The experimental results show that our method is better than the traditional method.

Original languageEnglish
Title of host publicationNext-Generation Applied Intelligence - 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Proceedings
Pages644-652
Number of pages9
DOIs
Publication statusPublished - 2009 Nov 9
Event22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009 - Tainan, Taiwan
Duration: 2009 Jun 242009 Jun 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5579 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009
CountryTaiwan
CityTainan
Period09-06-2409-06-27

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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