1. Automatic Chinese Text Categorization Feature Engineering and Comparison of Classification Approaches
Yi-An Lin and Yu-Te Lin

2. Motivation
Text categorization (TC) is extensively researched in English but not in Chinese.
What’s feature engineering help in Chinese?
Should Chinese content be segmented?
What’s ML best for TC? – Naïve Bayes, SVM, Decision Tree, k Nearest Neighbor, MaxEnt, or Language Model Methods?

3. Outline Data Preparation Feature Selection Feature Vector Encoding
Comparison of Classifiers
Feature Engineering
Comparison after Feature Engineering
Conclusion

4. Data Preparation Tool: Yahoo News Crawler Category Entertainment
Politics
Business
Sports

5. Feature Selection
statistics:

6. Top Features by Term Meaning 職棒 pro. baseball 總統 president 美元 USD 網賽
tennis competition
馬英九
name
公開賽
open tournament
民進黨
DP Party 中華
Chinese nation 油價
oil price 股
stocks 杯
cup 經濟
economics ？
question mark 電影
movies 投資
invest 歌手
singer

7. Feature Vector Encoding
Binary: whether contains a word.
Count: number of occurrence.
TF: ratio of words occurrence.
TF-IDF: with inverse document freq.

8. Comparison of different encoding
classifier
binary
count TF
TF-IDF NB
82.48%
80.90%
82.35%
82.13% DT
80.28%
80.00%
79.43%
80.58%
kNN
72.85%
77.73%
77.18%
81.78%
SVM
83.55%
79.90%
80.60%
84.48%

9. Classifier Comparison Ⅰ

10. Classifier Comparison Ⅱ

11. Feature Engineering Stop Terms: similar to stop words in English.
Group Terms: common substrings.
Key Terms: distinctive terms.
Key Term
Intuitive
Possible Category 球
Related ball games?
→ Sports 黨
Related to politics?
→ Politics 債
Related to business trades?
→ Business 劇
Related to drama?
→ Entertainment

12. Comparison of feature engineering methods
S: stop terms
G: group terms
K: key terms

13. Comparison after FE Method Accuracy SVM seg 93.66% SVM non-seg 91.65%
7-gram
93.70%
9-gram
94.20%

14. Conclusion N-gram model outperforms other methods:
Language Models’ nature: considering all features and avoid error-prone ones.
No restrictive independence (ex. NB).
Better smoothing.
Feature engineering also helps reducing the sparsity but may cause ambiguity.
Semantic understanding could be the next to try in future research.