TY - GEN
T1 - Journal article topic detection based on semantic features
AU - Wang, Hei Chia
AU - Huang, Tian Hsiang
AU - Guo, Jiunn Liang
AU - Li, Shu Chuan
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350625066&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350625066&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02568-6_65
DO - 10.1007/978-3-642-02568-6_65
M3 - Conference contribution
AN - SCOPUS:70350625066
SN - 3642025676
SN - 9783642025679
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 644
EP - 652
BT - Next-Generation Applied Intelligence - 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Proceedings
T2 - 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009
Y2 - 24 June 2009 through 27 June 2009
ER -