DocumentCode
506904
Title
Automatic Genre Classification by Using Co-training
Author
Liu, Rui ; Jiang, Minghu ; Tie, Zheng
Author_Institution
Lab. of Comput. Linguistics, Tsinghua Univ., Beijing, China
Volume
1
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
129
Lastpage
132
Abstract
Researchers have concentrated on topic-based text classification while the genre of a document is rarely considered. In this article, we discuss the automatic genre classification and its application. We argue that word level features and sentence level features are two important measures which vary in number among different genres. Word level features include word frequency and POS (part of speech) tag statistics. Sentence level features include grammar rules, which have strong relations between different genres. Based on the two aspects of view, we explore a robust approach where the co-training method is employed to obtain high effectiveness for genre classification.
Keywords
pattern classification; statistical analysis; text analysis; automatic genre classification; co-training; part of speech; tag statistics; topic-based text classification; word frequency; Computational linguistics; Frequency shift keying; Fuzzy systems; HTML; Robustness; Search engines; Speech; Text categorization; Uniform resource locators; Web pages; Co-training; genre classification; grammar rules;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location
Tianjin
Print_ISBN
978-0-7695-3735-1
Type
conf
DOI
10.1109/FSKD.2009.609
Filename
5358649
Link To Document