Rough set theory based automatic text categorization and the handling of semantic heterogeneity:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English Chinese |
| Veröffentlicht: |
Bonn
Informationszentrum Sozialwiss.
2006
|
| Schriftenreihe: | Forschungsberichte / Informationszentrum Sozialwissenschaften
8 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | 251 S. graph. Darst. |
| ISBN: | 382060149X |
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Datensatz im Suchindex
| _version_ | 1804135596294144000 |
|---|---|
| adam_text | Table of contents
Acknowledgments 5
Abstract 6
Table of contents 7
1 Introduction 11
1.1 Background 11
1.2 Outline of Text categorization methods 12
1.3 Aims and objectives 14
1.4 Structure of this book 14
2 Text categorization 16
2.1 Paradigm of text categorization 16
2.2 Text representation 17
2.2.1 Indexing granularity 17
2.2.2 Text preprocessing 18
2.2.3 Term weighting schemes 18
2.3 Dimensionality reduction 20
2.3.1 Feature selection 20
2.3.1.1 Document Frequency 20
2.3.1.2 Information Gain 21
2.3.1.3 x2 statistic 21
2.3.1.4 Mutual Information 21
2.3.2 Feature extraction 22
2.3.2.1 Latent semantic indexing 22
2.3.2.2 Term clustering 24
2.3.3 Other methods 25
2.4 Text classifiers 25
2.4.1 Training set, test set and validation set 25
2.4.2 Support vector machine 25
2.4.2.1 Training the SVM in the separable case 25
2.4.2.2 Training the SVM in the non separable case 27
2.4.3 Neural network 28
2.4.4 Naive Bayes 28
2.4.5 Jfe Nearest neighbor 29
2.4.6 Linear classifiers 30
2.4.6.1 Rocchio 30
2.4.6.2 Linear discriminant analysis 31
2.4.6.3 Widrow Hoff algorithm 31
2.4.6.4 Exponentiated gradient algorithm 32
2.4.6.5 Linear Least Squares Fit 32
2.4.7 Rule based classifiers 33
2.4.7.1 RIPPER 33
2.4.7.2 Decision tree 34
o
Xueying Zhang
2.4.7.3 Fuzzy sets 35
2.4.8 Voted classifiers 35
2.4.8.1 Bagging algorithm 35
2.4.8.2 Boosting algorithm 37
2.4.9 Combined classifiers 38
2.5 Thresholding strategies 38
2.5.1 RCut 39
2.5.2 PCut 39
2.5.3 SCut 39
2.5.4 RTCut 39
2.6 Standard test collections 40
2.6.1 English test collections 40
2.6.1.1 OHSUMD 40
2.6.1.2 Reuters 1987 collection 41
2.6.1.3 TREC AP 42
2.6.2 Chinese test collections 42
2.6.2.1 TREC Mandarin Corpus 42
2.6.2.2 CWT test collection 42
2.6.2.3 Other collections 43
2.7 Performance evaluation measures 43
2.7.1 Binary classification tasks 43
2.7.2 Multi label classification tasks 46
2.8 Applications of text categorization 46
2.8.1 Automatic indexing 46
2.8.2 Relevance feedback 47
2.8.3 Text filtering 47
2.8.4 Natural language processing 48
2.9 Conclusion 48
3 Rough set theory in classification tasks 49
3.1 Basic rough set theory 49
3.2 A general scheme of classification systems 53
3.3 Discretization 53
3.4 Attribute reduction 54
3.4.1 Core reduct 54
3.4.2 Dynamic reduct 54
3.4.3 Heuristics reduct 55
3.4.3.1 Significance oriented method 55
3.4.3.2 Support oriented method 57
3.4.3.3 Average support method 57
3.4.3.4 Parameterized average support method 57
3.5 Decision rules 58
3.5.1 Basic decision rules 58
3.5.2 Approximate decision rules 58
3.6 Rule quality measures 59
i
Rough Set Tlieory based Automatic Text Categorization and... 9
3.6.1 Empirical measures 60
3.6.2 Discrimination parameter 60
3.6.3 Agreement formulas 61
3.6.4 Association measures 62
3.6.5 Information Score 62
3.6.6 Logical sufficiency 62
3.7 Rule matching 63
3.7.1 Complete matching 63
3.7.2 Partial matching 64
3.8 Advantages of rough set theory in classification tasks 64
3.9 Conclusion 65
4 Language independent text representation 66
4.1 Introduction 66
4.1.1 Text representation of English documents 66
4.1.2 Text representation of Chinese documents 67
4.1.2.1 Character level approaches 67
4.1.2.2 Vocabulary matching approaches 68
4.1.2.3 Word segmentation plus term weighting schemes 68
4.1.2.4 N gram based statistical methods 68
4.2 Outline of the GFGL approach 69
4.2.1 Special characters 70
4.2.2 Stop patterns 70
4.2.3 Text preprocessing 71
4.2.4 Splitting text into sentences 71
4.2.5 Creating N grams 71
4.3 N gram weighting scheme 72
4.4 Keyword filtering algorithm 73
4.5 Experiments and evaluation 75
4.5.1 Conventional measures 75
4.5.2 Retrieval performance in text categorization 77
4.5.2.1 Experiment on the CWT collection 77
4.5.2.2 Experiment on the Reuters 21578 82
4.6 The impact of the parameter k values 85
4.7 Conclusion 86
5 Semantic heterogeneity: Concept integration of indexing terms 87
5.1 Introduction 87
5.2 Rough set theory based transfer model 90
5.3 Outline of an RST based approach 93
5.4 Case study 94
5.4.1 One to one transfer relations 94
5.4.2 One to multiple transfer relations 97
5.4.3 Multiple to multiple transfer relations 98
5.4.4 Integration of transfer relations 100
5.5 Experimental evaluation 101
10 Xueying Zhang
5.6 Conclusion 104
6 Rough set theory based text categorization 104
6.1 Introduction 104
6.2 Representation of train documents 106
6.3 A general rough set theory based text categorization scheme 107
6.4 Experimental collection 108
6.5 Performance measures 108
6.6 Experimental Baseline 109
6.7 Synonym detection 110
6.8 Attribute classification significance coefficient 112
6.9 Induction of Classification rules 115
6.9.1 Basic classification rules 115
6.9.2 Approximate classification rules 119
6.10Rule matching measures 120
6.10.1 Modification of complete matching measures 120
6.10.2 Modification of partial matching measures 121
6.11 Dynamic category expansion 122
6.11.1 Dynamic category expansion algorithm 123
6.11.2 Experimental evaluation 125
6.12 Conclusion 126
7 Conclusions and future research 128
7.1 Conclusions 128
7.2 Future research 129
8 Reference 131
9 List of tables 140
10 List of figures 142
Appendix 1 CWT classification scheme 143
|
| adam_txt |
Table of contents
Acknowledgments 5
Abstract 6
Table of contents 7
1 Introduction 11
1.1 Background 11
1.2 Outline of Text categorization methods 12
1.3 Aims and objectives 14
1.4 Structure of this book 14
2 Text categorization 16
2.1 Paradigm of text categorization 16
2.2 Text representation 17
2.2.1 Indexing granularity 17
2.2.2 Text preprocessing 18
2.2.3 Term weighting schemes 18
2.3 Dimensionality reduction 20
2.3.1 Feature selection 20
2.3.1.1 Document Frequency 20
2.3.1.2 Information Gain 21
2.3.1.3 x2 statistic 21
2.3.1.4 Mutual Information 21
2.3.2 Feature extraction 22
2.3.2.1 Latent semantic indexing 22
2.3.2.2 Term clustering 24
2.3.3 Other methods 25
2.4 Text classifiers 25
2.4.1 Training set, test set and validation set 25
2.4.2 Support vector machine 25
2.4.2.1 Training the SVM in the separable case 25
2.4.2.2 Training the SVM in the non separable case 27
2.4.3 Neural network 28
2.4.4 Naive Bayes 28
2.4.5 Jfe Nearest neighbor 29
2.4.6 Linear classifiers 30
2.4.6.1 Rocchio 30
2.4.6.2 Linear discriminant analysis 31
2.4.6.3 Widrow Hoff algorithm 31
2.4.6.4 Exponentiated gradient algorithm 32
2.4.6.5 Linear Least Squares Fit 32
2.4.7 Rule based classifiers 33
2.4.7.1 RIPPER 33
2.4.7.2 Decision tree 34
o
Xueying Zhang
2.4.7.3 Fuzzy sets 35
2.4.8 Voted classifiers 35
2.4.8.1 Bagging algorithm 35
2.4.8.2 Boosting algorithm 37
2.4.9 Combined classifiers 38
2.5 Thresholding strategies 38
2.5.1 RCut 39
2.5.2 PCut 39
2.5.3 SCut 39
2.5.4 RTCut 39
2.6 Standard test collections 40
2.6.1 English test collections 40
2.6.1.1 OHSUMD 40
2.6.1.2 Reuters 1987 collection 41
2.6.1.3 TREC AP 42
2.6.2 Chinese test collections 42
2.6.2.1 TREC Mandarin Corpus 42
2.6.2.2 CWT test collection 42
2.6.2.3 Other collections 43
2.7 Performance evaluation measures 43
2.7.1 Binary classification tasks 43
2.7.2 Multi label classification tasks 46
2.8 Applications of text categorization 46
2.8.1 Automatic indexing 46
2.8.2 Relevance feedback 47
2.8.3 Text filtering 47
2.8.4 Natural language processing 48
2.9 Conclusion 48
3 Rough set theory in classification tasks 49
3.1 Basic rough set theory 49
3.2 A general scheme of classification systems 53
3.3 Discretization 53
3.4 Attribute reduction 54
3.4.1 Core reduct 54
3.4.2 Dynamic reduct 54
3.4.3 Heuristics reduct 55
3.4.3.1 Significance oriented method 55
3.4.3.2 Support oriented method 57
3.4.3.3 Average support method 57
3.4.3.4 Parameterized average support method 57
3.5 Decision rules 58
3.5.1 Basic decision rules 58
3.5.2 Approximate decision rules 58
3.6 Rule quality measures 59
i
Rough Set Tlieory based Automatic Text Categorization and. 9
3.6.1 Empirical measures 60
3.6.2 Discrimination parameter 60
3.6.3 Agreement formulas 61
3.6.4 Association measures 62
3.6.5 Information Score 62
3.6.6 Logical sufficiency 62
3.7 Rule matching 63
3.7.1 Complete matching 63
3.7.2 Partial matching 64
3.8 Advantages of rough set theory in classification tasks 64
3.9 Conclusion 65
4 Language independent text representation 66
4.1 Introduction 66
4.1.1 Text representation of English documents 66
4.1.2 Text representation of Chinese documents 67
4.1.2.1 Character level approaches 67
4.1.2.2 Vocabulary matching approaches 68
4.1.2.3 Word segmentation plus term weighting schemes 68
4.1.2.4 N gram based statistical methods 68
4.2 Outline of the GFGL approach 69
4.2.1 Special characters 70
4.2.2 Stop patterns 70
4.2.3 Text preprocessing 71
4.2.4 Splitting text into sentences 71
4.2.5 Creating N grams 71
4.3 N gram weighting scheme 72
4.4 Keyword filtering algorithm 73
4.5 Experiments and evaluation 75
4.5.1 Conventional measures 75
4.5.2 Retrieval performance in text categorization 77
4.5.2.1 Experiment on the CWT collection 77
4.5.2.2 Experiment on the Reuters 21578 82
4.6 The impact of the parameter k values 85
4.7 Conclusion 86
5 Semantic heterogeneity: Concept integration of indexing terms 87
5.1 Introduction 87
5.2 Rough set theory based transfer model 90
5.3 Outline of an RST based approach 93
5.4 Case study 94
5.4.1 "One to one" transfer relations 94
5.4.2 "One to multiple" transfer relations 97
5.4.3 "Multiple to multiple" transfer relations 98
5.4.4 Integration of transfer relations 100
5.5 Experimental evaluation 101
10 Xueying Zhang
5.6 Conclusion 104
6 Rough set theory based text categorization 104
6.1 Introduction 104
6.2 Representation of train documents 106
6.3 A general rough set theory based text categorization scheme 107
6.4 Experimental collection 108
6.5 Performance measures 108
6.6 Experimental Baseline 109
6.7 Synonym detection 110
6.8 Attribute classification significance coefficient 112
6.9 Induction of Classification rules 115
6.9.1 Basic classification rules 115
6.9.2 Approximate classification rules 119
6.10Rule matching measures 120
6.10.1 Modification of complete matching measures 120
6.10.2 Modification of partial matching measures 121
6.11 Dynamic category expansion 122
6.11.1 Dynamic category expansion algorithm 123
6.11.2 Experimental evaluation 125
6.12 Conclusion 126
7 Conclusions and future research 128
7.1 Conclusions 128
7.2 Future research 129
8 Reference 131
9 List of tables 140
10 List of figures 142
Appendix 1 CWT classification scheme 143 |
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| spellingShingle | Zhang, Xueying Rough set theory based automatic text categorization and the handling of semantic heterogeneity Kategorisierung (DE-588)4163445-7 gnd Computerlinguistik (DE-588)4035843-4 gnd Textverarbeitung (DE-588)4059667-9 gnd |
| subject_GND | (DE-588)4163445-7 (DE-588)4035843-4 (DE-588)4059667-9 (DE-588)4113937-9 |
| title | Rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| title_auth | Rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| title_exact_search | Rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| title_exact_search_txtP | Rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| title_full | Rough set theory based automatic text categorization and the handling of semantic heterogeneity Xueying Zhang |
| title_fullStr | Rough set theory based automatic text categorization and the handling of semantic heterogeneity Xueying Zhang |
| title_full_unstemmed | Rough set theory based automatic text categorization and the handling of semantic heterogeneity Xueying Zhang |
| title_short | Rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| title_sort | rough set theory based automatic text categorization and the handling of semantic heterogeneity |
| topic | Kategorisierung (DE-588)4163445-7 gnd Computerlinguistik (DE-588)4035843-4 gnd Textverarbeitung (DE-588)4059667-9 gnd |
| topic_facet | Kategorisierung Computerlinguistik Textverarbeitung Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2704442&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014956658&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV021863396 |
| work_keys_str_mv | AT zhangxueying roughsettheorybasedautomatictextcategorizationandthehandlingofsemanticheterogeneity |