Title :
Notice of Retraction
Study on Method of Word Segmentation in Feature Selection in Chinese Text Categorization
Author :
Huang Wei ; Liu Yi ; Gao Bing ; Yang Ke-wei
Author_Institution :
Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Since the automatic word segmentation of Chinese text will bring the lack of information, method of word segmentation according to lexical chunk as segmentation unit are proposed. Use traditional segmentation method segment Chinese text based calculate mutual information between two lexical entries and adjacent frequency of two or more lexical entries, according to this calculated value judge and sign the lexical chunk by relevant words. The experimentation shows that after the word combination, the lexical chunk bear much more feature information which shares a better effect of the process. It also has proved the effect of feature selection in Chinese text categorization and enhanced the capability of text classification.
Keywords :
classification; natural language processing; text analysis; Chinese text categorization; feature selection; lexical chunk; text classification; word combination; word segmentation; Conference management; Data mining; Educational institutions; Electronic mail; Information management; Knowledge management; Management information systems; Mutual information; Technology management; Text categorization;
Conference_Titel :
Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-1-4244-5397-9
DOI :
10.1109/WKDD.2010.61