Enhancing Naive Bayes with various smoothing methods for short text classification

WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Volumn , Issue , 2012, Pages 645-646

  Yuan, Quan ^a   Cong, Gao ^a   Thalmann, Nadia M ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Naive Bayes; Short text classification; Smoothing

Indexed keywords

CLASSIFICATION MODELS; MICRO-BLOG; NAIVE BAYES; QUESTION CLASSIFICATION; SMOOTHING; SMOOTHING METHODS; TEXT CLASSIFICATION; TOPIC CLASSIFICATION; TRAINING DATA;

WORLD WIDE WEB;

CLASSIFIERS;

EID: 84861084022     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2187980.2188169     Document Type: Conference Paper

References (4)

1. D. Koller and M. Sahami. Hierarchically classifying documents using very few words. In ICML, pp.170-178, 1997.
2. Jing Bai and Jian-Yun Nie. Using Language Models for Text Classification. In AIRS, 2004.
3. X.-H. Phan, L.-M. Nguyen, and S. Horiguchi. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In mfW/pp.91-100, 2008.
4. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. TOIS, 22:179-214, 2004.