1. SoC Presentation Title 2004 A New Term Weighting Method for Text Categorization LAN Man School of Computing National University of Singapore 16 Apr, 2007

2. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

3. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

4. SoC Presentation Title 2004 Introduction Background Explosive increase of textual information Organizing and accessing these information in flexible ways Text Categorization (TC) is the task of classifying natural language documents into a predefined set of semantic categories

5. SoC Presentation Title 2004 Categorize web pages by topic (the directories like Yahoo!); Customize online newspapers to different labels according to a particular user’s reading preferences; Filter spam emails and forward incoming emails to the target expert by content; Word sense disambiguation is also taken as a text categorization task once we view word occurrence contexts as documents and word sense as categories. Introduction Applications of TC

6. SoC Presentation Title 2004 TC is a discipline at the crossroads of machine learning (ML) and information retrieval (IR) Two key issues in TC: – Text Representation – Classifier Construction This thesis focuses on the first issue. Introduction Two sub-issues of TC

7. SoC Presentation Title 2004 Approaches to build a classifier: – No more than 20 algorithms – Borrowed from Information Retrieval: Rocchio – Machine Learning: SVM, kNN, decision Tree, Naïve Bayesian, Neural Network, Linear Regression, Decision Rule, Boosting, etc. SVM performs better. Introduction Construction of Text Classifier

8. SoC Presentation Title 2004 Little room to improve the performance from the algorithm aspect: – Excellent algorithms are few. – The rationale is inherent to each algorithm and the method is usually fixed for one given algorithm – Tuning the parameter has limited improvement Introduction Construction of Text Classifier

9. SoC Presentation Title 2004 Various text format, such as DOC, PDF, PostScript, HTML, etc. Can Computer read them like us? Convert them into a compact format in order to be recognized and categorized for classifiers or a classifier-building algorithm in a computer. This indexing procedure is also called text representation. Introduction Text Representation

10. SoC Presentation Title 2004 Texts are vectors in the term space. Assumption: Documents that are “close together” in space are similar in meaning. Introduction Vector Space Model

11. SoC Presentation Title 2004 Two main issues in Text Representation: 1. What should a term be – Term Type 2. How to weight a term – Term Weighting Introduction Text Representation

12. SoC Presentation Title 2004 Sub-word level – syllables Word level – single token Multi-word level – phrases, sentences, etc Syntactic and semantic – sense (meaning) Introduction 1. What Should a Term be?

13. SoC Presentation Title 2004 1. Word senses (meanings) [Kehagias 2001] same word assumes different meanings in a different contexts 2. Term clustering [Lewis 1992] group words with high degree of pairwise semantic relatedness 3. Semantic and syntactic representation [Scott & matwin 1999] Relationship between words, i.e. phrases, synonyms and hypernyms Introduction 1. What Should a Term be?

14. SoC Presentation Title 2004 4. Latent Semantic Indexing [Deerwester 1990] A feature reconstruction technique 5. Combination Approach [Peng 2003] Combine two types of indexing terms, i.e. words and 3-grams 6. Theme Topic Mixture Model – [Keller 2004] Graphical Model 7. Using keywords from summarization [Li 2003] Introduction 1. What Should a Term be?

15. SoC Presentation Title 2004 In general, high level representation did not show good performance in most cases Word level is better (bag-of-words) Introduction 1. What Should a Term be?

16. SoC Presentation Title 2004 [Salton 1988] elaborated three considerations: –1. term occurrences closely represent the content of document –2. other factors with the discriminating power pick up the relevant documents from other irrelevant documents –3. consider the effect of length of documents Introduction 2. How to Weight a Term?

17. SoC Presentation Title 2004 Simplest method – binary Most popular method – tf.idf Combination with information-theory metrics or statistics method – tf.chi2, tf.ig, tf.gr Combination with the linear classifier Introduction 2. How to Weight a Term?

18. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

19. SoC Presentation Title 2004 SVM is better. Leopold(2002) stated that text representation dominates the performance of text categorization rather than the kernel functions of SVMs. Which is the best method for SVM-based text classifier among these widely-used ones? Motivation (1)

20. SoC Presentation Title 2004 Text categorization is a form of supervised learning The prior information on the membership of training documents in predefined categories is useful in – feature selection – supervised learning for classifier Motivation (2)

21. SoC Presentation Title 2004 The supervised term weighting methods adopt this known information and consider the document distribution. They are naturally expected to be superior to the unsupervised (traditional) term weighting methods. Motivation (2)

22. SoC Presentation Title 2004 Q1. How can we propose a new effective term weighting method by using the important prior information given by the training data set? Q2. Which is the best term weighting method for SVM-based text classifier? Motivation Three questions to be addressed

23. SoC Presentation Title 2004 Q3. Are supervised term weighting methods able to lead to better performance than unsupervised ones for TC? What kinds of relationship can we find between term weighting methods and the widely-used learning algorithms, i.e. kNN and SVM, given different benchmark data collections? Motivation Three questions to be addressed

24. SoC Presentation Title 2004 First, we will analyze term’s discriminating power for TC and propose a new term weighting method using the prior information of the training data set to improve the performance of TC. Motivation Sub-tasks of This Thesis

25. SoC Presentation Title 2004 Second, we will explore term weighting methods for SVM-based text categorization and investigate the best method for SVM- based text classifier. Motivation Sub-tasks of This Thesis

26. SoC Presentation Title 2004 Third, we will extend research work on more general benchmark data sets and learning algorithms to examine the superiority of supervised term weighting methods and investigate the relationship between term weighting methods and different learning algorithms. Moreover, this study will be extended to a new application domain, i.e. biomedical literature classification. Motivation Sub-tasks of This Thesis

27. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

28. SoC Presentation Title 2004 Machine Learning Algorithms – SVM, kNN Benchmark Data Corpora – Reuters News Corpus – 20Newsgroups Corpus – Ohsumed Corpus – 18Journal Corpus Methodology of Research

29. SoC Presentation Title 2004 Evaluation Measures – F1 – Breakeven point Significance Tests – McNemar’s significance Test Methodology of Research

30. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

31. SoC Presentation Title 2004 Three considerations: 1. Term occurrence: binary, tf, ITF, log(tf) 2. Term’s discriminative power: idf Note: chi^2, ig (information gain), gr (gain ratio), mi (mutual information), or (Odds Ratio), etc. 3. Document length: cosine normalization, linear normalization Analysis and Proposal of a New Term Weighting Method

32. SoC Presentation Title 2004 Analysis and Proposal of a New Term Weighting Method Analysis of Term’s Discriminating Power

33. SoC Presentation Title 2004 Assume they have same tf value. t1, t2, t3 share the same idf1 value; t4, t5, t6 share the same idf2 value. Clearly, the six terms contribute differently to the semantic of documents. Analysis and Proposal of a New Term Weighting Method Analysis of Term’s Discriminating Power

34. SoC Presentation Title 2004 Case 1. t1 contributes more than t2 and t3; t4 contributes more than t5 and t6. Case 2. t4 contributes more than t1 although idf(t4) < idf(t1). Analysis and Proposal of a New Term Weighting Method Term’s Discriminating Power -- idf

35. SoC Presentation Title 2004 Case 3. for t1, t2, t3, in idf value, t1= t2= t3 in chi^2 value, t1=t3 >t2 in or value, t1 > t2 > t3 Case 4. for t1 and t4, in idf value, t1 > t4 in chi^2 and or value, t1 < t4 Analysis and Proposal of a New Term Weighting Method Term’s Discriminating Power -- idf, chi^2, or

36. SoC Presentation Title 2004 Intuitive Consideration the more concentrated a high frequency term is in the positive category than in the negative category, the more contributions it makes in selecting the positive samples from among the negative samples. Analysis and Proposal A New Term Weighting Method -- rf

37. SoC Presentation Title 2004 rf – relevance frequency The rf is only related to the ratio of b and c, not involve d The base of log is 2. in case of c=0, c=1 The final weight of term is: tf*rf Analysis and Proposal A New Term Weighting Method -- rf

38. SoC Presentation Title 2004 Analysis and Proposal Empirical observation feature Category: 00_acq idfrfchi2origgr Acquir3.5534.368850.6630.6680.1250.161 Stake4.2012.975303.9424.4270.0740.096 Payout4.999110.870.0140.0110.014 dividend3.5671.03346.630.1420.0170.022 Comparison of idf, rf, chi2, or, ig and gr value of four features in category 00_acq of Reuters Corpus

39. SoC Presentation Title 2004 Analysis and Proposal Empirical observation feature Category: 03_earn idfrfchi2origgr Acquir3.5531.07481.500.1390.0310.032 Stake4.2011.08231.260.1640.018 Payout4.9997.82044.68364.3270.0410.042 dividend3.5674.408295.4635.8410.0920.095 Comparison of idf, rf, chi2, or, ig and gr value of four features in category 03_earn of Reuters Corpus

40. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

41. SoC Presentation Title 2004 Purposes of Experiment Set 1: 1. Explore the best term weighting method for SVM-based text categorization – (Q2) 2. Compare tf.rf with various traditional term weighting methods on SVM – (Q1) Experiment Set 1 Exploring the best term weighting method for SVM-based text categorization

42. SoC Presentation Title 2004 MethodsDenotationDescription Traditional Weighting Methods Term frequency binary0: absence, 1: presence tfTerm frequency alone logtflog(1+tf) ITF1-1/(1+tf) tf.idf And variants idfidf alone tf.idfClassical tf * idf logtf.idflog(1+tf) * idf tf. idf_probtf * Probabilistic idf Supervised Weighting Methods tf.chi^2tf * chi square Our new tf.rftf.rfOur new method Experiment Set 1 Experimental Methodology: 10 Methods

43. SoC Presentation Title 2004 Experiment Set 1 Results on Reuters Corpus

44. SoC Presentation Title 2004 Experiment Set 1 Results on subset of 20Newsgroups Corpus

45. SoC Presentation Title 2004 tf.rf performs better consistently No significant difference among the three term frequency variants, i.e. tf, logtf and ITF No significant difference between tf.idf, logtf.idf and tf.idf-prob Experiment Set 1 Conclusions (1)

46. SoC Presentation Title 2004 idf and chi^2 factor, even considering the distribution of documents, do not improve or even decrease the performance binary and tf.chi^2 significantly underperform the other methods Experiment Set 1 Conclusions (2)

47. SoC Presentation Title 2004 Purposes of Experiment Set 2: 1. Investigate supervised term weighting methods and their relationship with learning algorithms – (Q3) 2. Compare the effectiveness of tf.rf under more general experimental circumstances – (Q1) Experiment Set 2 Investigating supervised term weighting method and their relationship with learning algorithms

48. SoC Presentation Title 2004 Supervised term weighting methods – Use the prior information on the membership of training documents in predefined categories Unsupervised term weighting methods – Does not use. – binary, tf, log(1+tf), ITF – Most popular is tf.idf and its variants: logtf.idf, tf.idf-prob Experiment Set 2 Review

49. SoC Presentation Title 2004 1. Combined with information-theory functions or statistic metrics – such as chi2, information gain, gain ratio, Odds ratio, etc. – Used in the feature selection step – Select the most relevant and discriminating features for the classification task, that is, the terms with higher feature selection scores The results are inconsistent and/or incomplete. Experiment Set 2 Review: Supervised Term Weighting Methods

50. SoC Presentation Title 2004 2. Interaction with supervised text classifier –Linear SVM, Perceptron, kNN –Text classifier selects the positive test documents from negative test documents by assigning different scores to the test samples, these scores are believed to be effective in assigning more appropriate weights to terms Experiment Set 2 Review: Supervised Term Weighting Methods

51. SoC Presentation Title 2004 3. Based on Statistical Confidence Intervals -- ConfWeight – Linear SVM – Compared with tf.idf and tf.gr (gain ratio) – The results failed to show that supervised methods are generally higher than unsupervised methods. Experiment Set 2 Review: Supervised Term Weighting Methods

52. SoC Presentation Title 2004 Hypothesis: Since supervised schemes consider the document distribution, they should perform better than unsupervised ones Motivation: 1. Are supervised term weighting method is superior to the unsupervised (traditional) methods? 2. What kinds of relationship between term weighting methods and the learning algorithms, i.e. kNN and SVM, given different data collections? Experiment Set 2

53. SoC Presentation Title 2004 Unsupervised Term Weighting binary0 or 1 tfTerm frequency tf.idfClassic scheme Supervised Term Weighting tf.rfOwn scheme tf.chi^2Chi^2 tf.igInformation gain tf.orOdds ratio Experiment Set 2 Methodology

54. SoC Presentation Title 2004 Experiment Set 2 Results on Reuters Corpus using SVM

55. SoC Presentation Title 2004 Experiment Set 2 Results on Reuters Corpus using kNN

56. SoC Presentation Title 2004 Experiment Set 2 Results on 20Newsgroups Corpus using SVM

57. SoC Presentation Title 2004 Experiment Set 2 Results on 20Newsgroups Corpus using kNN

58. SoC Presentation Title 2004 AlgorithmCorpus#_fea Significance Test Results SVMR15937 (tf.rf, tf, rf) > tf.idf > (tf.ig, tf.chi2, binary) >> tf.or SVM2013456 (rf, tf.rf, tf.idf) > tf >> binary >> tf.or >> (tf.ig, tf.chi2) kNNR405 (binary, tf.rf) > tf >> (tf.idf, rf, tf.ig) > tf.chi2 >> tf.or kNN20494 (tf.rf, binary, tf.idf,tf) >> rf >> (tf.or, tf.ig, tf.chi2) Experiment Set 2 McNemar’s Significance Tests

59. SoC Presentation Title 2004 Experiment Set 2 Effects of feature set size on algorithms For SVM, almost all methods achieved the best performance when in putting the full vocabulary (13000-16000 features) For kNN, the best performance achieved at a smaller feature set size (400-500 features) Possible reason: different noise resistance

60. SoC Presentation Title 2004 Q1: Are supervised term weighting methods superior to the unsupervised term weighting methods? -- No always. Experiment Set 2 Conclusions (1)

61. SoC Presentation Title 2004 Experiment Set 2 Conclusions (1) Specifically, three supervised methods based on information theory, i.e. tf.chi2, tf.ig and tf.or, perform rather poorly in all experiments. On the other hand, newly proposed supervised method, tf.rf achieved the best performance consistently and outperforms other methods substantially and significantly.

62. SoC Presentation Title 2004 Q2: What kinds of relationship between term weighting methods and learning algorithms, given different benchmark data collections? A: The performance of the term weighting methods, especially, the three unsupervised methods, has close relationships with the learning algorithms and data corpora. Experiment Set 2 Conclusions (2)

63. SoC Presentation Title 2004 Summary of supervised methods: – tf.rf performs consistently better in all experiments; – tf.or, tf.chi^2 and tf.ig perform consistently the worst in all experiments; – rf alone, shows a comparable performance to tf.rf except for on Reuters using kNN Experiment Set 2 Conclusions (4)

64. SoC Presentation Title 2004 Summary of unsupervised methods: – tf.idf performs comparable to tf.rf on the uniform category corpus either using SVM or kNN; – binary performs comparable to tf.rf on both corpora using kNN while rather bad using SVM; – although tf does not perform as well as tf.rf, it performs consistently well in all experiments Experiment Set 2 Conclusions (5)

65. SoC Presentation Title 2004 Purpose: Application on biomedical data collections Motivation: Explosive growth of biomedical research Experiment Set 3 Applications on Biomedical Domain

66. SoC Presentation Title 2004 The Ohsumed Corpus Used by Joachims [1998] 18 Journals Corpus – Biochemistry and Molecular Biology – Top impact factor – 7903 documents from two-year span (2004 - 2005) Experiment Set 3 Data Corpora

67. SoC Presentation Title 2004 Four term weighting methods bianry, tf, tf.idf, tf.rf Evaluation Measures – Micro-averaged breakeven point – F1 Experiment Set 3 Methodology

68. SoC Presentation Title 2004 Experiment Set 3 Results on the Ohsumed Corpus

69. SoC Presentation Title 2004 schemeMicro-RMicro-PMicro-F1Macro-F1 binary0.60910.60970.60940.5757 tf0.65780.65660.65720.6335 tf.idf0.65670.65880.65780.6407 tf.rf0.68100.68000.68050.6604 Experiment Set 3 The best performance of SVM with four term weighting schemes on the Ohsumed Corpus

70. SoC Presentation Title 2004 Our linear SVM is more accurate – 68.05% (linear) vs 60.7%(linear) and 66.1%(rbf) The performances of tf.idf are identical – 65.78% vs 66% Our proposed tf.rf performs significantly better than other methods. Experiment Set 3 Comparison between our study and Joachims’ study

71. SoC Presentation Title 2004 Experiment Set 3 Results on 18 Journals Corpus

72. SoC Presentation Title 2004 Experiment Set 3 Results on 3 subsets of 18 Journals Corpus

73. SoC Presentation Title 2004 The comparison between our results and Joachims’ results shows that tf.rf can improve on the classification performance than tf.idf. tf.rf outperforms the other term weighting methods on the both data corpora. tf.rf can improve the classification performance of biomedical text classification. Experiment Set 3 Conclusions

74. SoC Presentation Title 2004 Outline Introduction Motivation Methodology of Research Analysis and Proposal of a New Term Weighting Method Experimental Research Work Contributions and Future Work

75. SoC Presentation Title 2004 Contributions of Thesis 1.To propose an effective supervised term weighting method tf.rf to improve the performance of text categorization.

76. SoC Presentation Title 2004 Contributions of Thesis 2. To make an extensive comparative study of different term weighting methods under controlled conditions.

77. SoC Presentation Title 2004 Contributions of Thesis 3. To give a deep analysis of the terms’ discriminating power for text categorization from the qualitative and quantitative aspects.

78. SoC Presentation Title 2004 Contributions of Thesis 4. To investigate the relationships between term weighting methods and learning algorithms given different corpora.

79. SoC Presentation Title 2004 Future Work (1) Extending term weighting methods on feature types other than word

80. SoC Presentation Title 2004 Future Work (2) 2. Applying term weighting methods to other text-related applications

81. SoC Presentation Title 2004 Thanks for your time and suggestion.