1. Reduction of Training Noises for Text Classifiers Rey-Long Liu Dept. of Medical Informatics Tzu Chi University Taiwan

2. Outline Background Problem definition The proposed approach: TNR Empirical evaluation Conclusion Training Noise Reduction for TC2

3. Background Training Noise Reduction for TC3

4. Training of Text Classifiers Given –Training documents labeled with category labels Return –Text classifiers that can Classify in-space documents (those that are relevant to some categories) Filter out out-space documents (those that are not relevant to any of the categories of interest) Usage: retrieval and dissemination of information Training Noise Reduction for TC4

5. Typical Problem: Noises in the Training Texts The training documents are inevitably unsound and/or incomplete –A lot of noises in the training texts Those terms that are irrelevant but happen to appear in the training documents Training Noise Reduction for TC5

6. Problem Definition Training Noise Reduction for TC6

7. Goal & Motivation Goal –Develop a technique TNR (Training Noise Reduction) that removes possible training noises for text classifiers Motivation –With the help by TNR, text classifiers can be trained to have better performance in Classifying in-space documents Filtering out out-space documents Training Noise Reduction for TC7

8. Basic Idea Term proximity as the key evidence to identify noises –In a training text d of a category c, a sequence of consecutive terms (in d) are noises if they have many neighboring terms not related to c –They are noises because they may simply happen to appear in d and hence are likely to be irrelevant to c Training Noise Reduction for TC8

9. Related Work No previous approaches focused on the fusion of relatedness scores of consecutive terms to identify training noises for text classifiers –Term proximity was mainly employed to improve text ranking or select features to build text classifiers TNR can serve as a front-end processor for the techniques of feature selection and classifier development (e.g., SVM) Training Noise Reduction for TC 9

10. The Proposed Approach: TNR Training Noise Reduction for TC10

11. Basic Definition Positive correlation vs. Negative correlation –A term t is positively correlated to a category c if occurrence of t in a document d increases the possibility of classifying d into c; otherwise t is negatively correlated to c Therefore, TNR should remove those terms that are negatively correlated to c Training Noise Reduction for TC11

12. Training Noise Reduction for TC12

13. The Main Hypothesis Those terms (in d) that have many neighboring terms with negative or low correlation strengths to c may simply happen to appear in d and hence are likely to be the training noises in d Training Noise Reduction for TC13

14. The Algorithm of TNR (1) For a category c, sequentially scan each term t in d (1.1) Employ the  2 statistics to compute the cumulative correlation strength at t Positive correlation if t is more likely to appear in documents of categories c; otherwise negative correlation Positive correlation  Increase net strength (NetS <= 0) Negative correlation  Decrease net strength (2) Identify the term segments (in d) that are likely to be training noises (2.1) Noise = the text segments that are more likely to contain FP and TN terms Training Noise Reduction for TC14

15. Training Noise Reduction for TC15 The position at which the net strength is minimized The position at which the net strength becomes negative The training noise identified

16. Empirical Evaluation Training Noise Reduction for TC16

17. Experimental Data Top-10 fatal diseases and top-20 cancers in Taiwan –# of diseases: 28 –# of documents: 4669 (of 5 aspects: etiology, diagnosis, treatment, prevention, and symptom) –Source: Web sites of hospitals, healthcare associations, and department of health in Taiwan –Training documents: 2300 documents –Test documents: In-space documents: The remaining 2369 documents Out-space documents: 446 documents about other diseases Training Noise Reduction for TC17

18. Underlying Classifiers Underlying classifier –The Support Vector Machine (SVM) classifier Training Noise Reduction for TC18

19. Results: Classification of In- Space Documents Evaluation criteria –Micro-averaged F 1 (MicroF 1 ) –Macro-averaged F 1 (MacroF 1 ) Training Noise Reduction for TC19

20. Training Noise Reduction for TC20

21. Training Noise Reduction for TC21

22. Results: Filtering of Out-Space Documents Evaluation criteria –Filtering ratio (FR) = # out-space documents successfully rejected by all categories / # out-space documents –Average number of misclassifications (AM) = # misclassifications for the out-space documents / # out-space documents Training Noise Reduction for TC22

23. Training Noise Reduction for TC23

24. Training Noise Reduction for TC24

25. Conclusion Training Noise Reduction for TC25

26. Text classifiers are essential for archival and dissemination of information Many text classifiers are built by a set of training documents –The training documents are inevitably unsound and incomplete, and so contain many training noises Reduction of the training noises can be done by analyzing correlation types of consecutive terms We show that the noise reduction is helpful in improving state-of-the-art text classifiers Training Noise Reduction for TC26