1. CATEGORIZATION OF NEWS ARTICLES USING NEURAL TEXT CATEGORIZER
FUZZ-IEEE 2009, Korea, August 20-24, 2009 Taeho Jo Inha University
Reporter:洪紹祥
Adviser:鄭淑真

2. OUTLINE Introduction Previous Works Framework Empirical Results
Conclusions

3. INTRODUCTION(1/2)
Text categorization is necessary for managing textual text as efficiently as possible.
Text categorization is requires the two manual preliminary:
The predefined of categorization
The preparation of sample labeled documents.

4. INTRODUCTION(2/2)
Traditional Text Categorization requires encoding documents into numerical vector
Cause two main problems:
Huge dimensionality.
Sparse distribution.
Solve the two main problem
Encoded into String Vector
Different from numerical Vector, words are given as feature value.
Propose a neural network, called NTC(Neural Text Categorizer)

5. PREVIOUS WORKS(1/2) Popular approaches for Text categorization
KNN(K Nearest Neighbor)
NB(Naïve Bayes)
SVM(Support Vector Machine)
Neural Networks
Causes two Main problems
Huge dimensionality.
Sparse distribution.
Using String kernel in SVM
Failed to improve the performance.

6. PREVIOUS WORKS(2/2) String kernel
Receives two raw texts as inputs and computes their syntactical similarity between them
Advantage
Don’t need to be encoded into numerical vector.
More transparent than numerical vector .
Easier to trace why each document is classified.
Disadvantages
Cost too much time for computing the similarity.

7. FRAMEWORK(1/2)
Bag of Words

8. FRAMEWORK(2/2)

9. EMPIRICAL RESULTS(1/3)
The collection of news articles, called NewsPage.
News articles
500 dimensional numerical vectors.
50 dimensional string vectors.

10. EMPIRICAL RESULTS(2/3)
The configuration of participating approaches

11. EMPIRICAL RESULTS(3/3)
The Results of This Set of Experiments

12. CONCLUSIONS
The four contributions are considered as the significance of this research.
According to the results of the set of experiments, this research proposes the practical approach.
It solved the two main problems,
the huge dimensionality
the sparse distribution
Created a new neural network, called NTC.
Provides the potential easiness for tracing why each document is classified so.