Title :
Optimizing Academic Conference Classification Using Social Tags
Author :
Xia, Jing ; Wen, Kunmei ; Li, Ruixuan ; Gu, Xiwu
Author_Institution :
Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
Automatically classifying academic conference into semantic topic promises improved academic search and browsing for users. Social tagging is an increasingly popular way of describing the topic of academic conference. However, no attention has been devoted to academic conference classification by making use of social tags. Motivated by this observation, this paper proposes a method which utilizes social tags as well as the content of academic conference in order to improve automatically identifying academic conference classification. The proposed method applies different automatic classification algorithms to improve classification quality by using social tags. Experimental results show that this method mentioned above performs better than the method which only utilizes the content to classify academic conference with 1% Precision measure score increase and 1.64% F1 measure score increase, which demonstrates the effectiveness of the proposed method.
Keywords :
classification; academic conference classification; automatic classification; browsing; improved academic search; semantic topic promises; social tagging; social tags; Classification algorithms; Prediction algorithms; Support vector machine classification; Testing; Training; Vocabulary; Web pages; academic conference; classification; feature selection;
Conference_Titel :
Computational Science and Engineering (CSE), 2010 IEEE 13th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-9591-7
Electronic_ISBN :
978-0-7695-4323-9
DOI :
10.1109/CSE.2010.43