An Efficient Centroid Based Chinese Web Page Classifier

1. An Efficient Centroid Based Chinese Web Page Classifier LIU Hui EE Dept of Tsinghua Univ. China Aug 28, 2003

2. Outline • Background • Basic Technique • Classifier Design & Implementation • Idea • Architecture • Feature • Experiment • Summary

3. Background of Web Page Classification • Explosive information need organization • Digital Library • Search Engine • Special (Categorized) Sites • Research hot points • Data Mining • Information Retrieval • Pattern Recognition • Text Automatic Categorization

4. Background of Our Classifier • Net-compass Search Engine • An emerging large and distributed search engine • Embedded in its new version • Chinese web page categorization competition • Held on March 14th –15th, 2003 • Ranked first • Workgroup • EE Dept of Tsinghua Univ., 3 master students & 1 undergraduate student

5. Basic Text Categorization System

6. Feature Selection • Term Frequency (TF) • Term Frequency & Inverse Document Frequency (TF.IDF) • Mutual Information (MI) • Statistics

7. Training - Statistical Machine Learning Vector Distance • Centroid Based Method • k-Nearest Neighbor: lazy learning • Support Vector Machine: Structural Risk Minimization Feedback & Combining Classifiers • Neuron Network • Boosting method Probability • Naïve Bayes: Pr (Term/Class) -> Pr(Text/Class)

8. Idea • Large Database Net-compass Search Engine • Fast Speed • Tolerable Precision • Web Resource Fast changing • Easy building Classifier Fast Training • Supporting multi-language • Word segmentation • Easy Training Set Building & Updating

9. Architecture

10. Features Preprocessing • Chinese Word Segmentation • Dictionary built on search engine log • Adaptability, Manageability, Accuracy • Maximum Matching Segmenting Method • Fast, tolerable accuracy • Noise Filtering • Stop word: common word, abandon word • Advertising links: length & content

11. Features • Combined Feature Selection • Statistics: tend to choose high-freq words • Mutual Information: tend to low-freq words • Subspace

12. Features • Adaptive Factors Adjust model, compensate for deficiency of training set • Class Weight • VIP word factor • Implementation • Berkeley DB • Structured dictionary • Avoid I/O • 3000 medium-sized Chinese Web page: 50 seconds

13. Experiment • Corpus • Chinese Web Page training set • Provided by Peking University • 11 classes, 14000 samples, much unbalanced distribution • Evaluation • Precision, Recall, F-measure

14. Experiment Result

15. Experiment Discussion • More samples, more accurate • Some classes are more difficult • Corpus cover not large enough • Open testing: 85% Relation between Precision and number of training samples

16. Summary An efficient Chinese Web Page Classifier • Clear Design • Centroid based, general steps • Novel Features • Preprocessing tricks • Combined feature selection • Subspace & Adaptive factors • Satisfactory Performance • Comparatively high accuracy • Very fast speed • High adaptability

17. Thank you all! Welcome any question 