TY  - CONF
AU  - Wang, Pu
AU  - Hu, Jian
AU  - Zeng, Hua-Jun
AU  - Chen, Lijun
AU  - Chen, Zheng
A2  - 
T1  - Improving Text Classification by Using Encyclopedia Knowledge
T2  - Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
PB  - 
CY  - 
PY  - 2007/
M2  - 
VL  - 
IS  - 
SP  - 332
EP  - 341
UR  - ftp://ftp.computer.org/press/outgoing/proceedings/icdm07/Data/3018a332.pdf
M3  - 10.1109/ICDM.2007.77
KW  - classification
KW  - text
KW  - ol
KW  - ****
KW  - learning
KW  - evaluation
KW  - ontology
KW  - wikipedia
L1  - 
SN  - 978-0-7695-3018-5
N1  - Welcome to IEEE Xplore 2.0: Improving Text Classification by Using Encyclopedia Knowledge
N1  - 
AB  - The exponential growth of text documents available on the Internet has created an urgent need for accurate, fast, and general purpose text classification algorithms. However, the "bag of words" representation used for these classification methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with this problem, we integrate background knowledge - in our application: Wikipedia - into the process of classifying text documents. The experimental evaluation on Reuters newsfeeds and several other corpus shows that our classification results with encyclopedia knowledge are much better than the baseline "bag of words " methods.
ER  -