Text mining for biology and biomedicine:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boston
Artech House
2006
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xi, 286 p. Ill., graph. Darst. 24 cm |
| ISBN: | 158053984X |
Internformat
MARC
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| 245 | 1 | 0 | |a Text mining for biology and biomedicine |c Sophia Ananiadou, John McNaught, editors |
| 264 | 1 | |a Boston |b Artech House |c 2006 | |
| 300 | |a xi, 286 p. |b Ill., graph. Darst. |c 24 cm | ||
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| 500 | |a Includes bibliographical references and index | ||
| 650 | 2 | |a Information Storage and Retrieval/trends | |
| 650 | 2 | |a Natural Language Processing | |
| 650 | 2 | |a Abstracting and Indexing/methods | |
| 650 | 4 | |a Biologie - Informatique | |
| 650 | 4 | |a Exploration de données (Informatique) | |
| 650 | 4 | |a Médecine - Informatique | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Medizin | |
| 650 | 4 | |a Biology |x Data processing | |
| 650 | 4 | |a Medicine |x Data processing | |
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Datensatz im Suchindex
| _version_ | 1804137899379130368 |
|---|---|
| adam_text | Contents
1 Introduction 2
1.1 Text Mining: Aims, Challenges, and Solutions 1
1.2 Outline of the Book 7
Acknowledgments 11
Conventions 11
References 11
2 Levels of Natural Language Processing for
Text Mining 13
2.1 Introduction 13
2.2 The Lexical Level of Natural Language Processing 16
2.2.1 Tokenization 16
2.2.2 Morphological Analysis 18
2.2.3 Linguistic Lexicons 19
2.3 The Syntactic Level of Natural Language Processing 21
2.3.1 Part-of-Speech Tagging 22
2.3.2 Chunking 23
2.3.3 Parsing 25
2.4 The Semantic Level of Natural Language Processing 25
v
vi Text Mining for Biology and Biomedicine
2.4.1 Lexical Semantic Interpretation 25
2.4.2 Semantic Interpretation of Utterances 27
2.5 Natural Language System Architecture for
Text Mining 31
2.5.1 General Architecture 31
2.5.2 Two Concrete System Architectures 35
2.6 Conclusions and Outlook 36
References 38
3 Lexical, Terminological, and Ontological Resources
for Biological Text Mining 43
3.1 Introduction 43
3.2 Extended Example 45
3.2.1 Entity Recognition 45
3.2.2 Relation Extraction 46
3.3 Lexical Resources 48
3.3.1 WordNet 48
3.3.2 UMLS Specialist Lexicon 49
3.3.3 Other Specialized Resources 49
3.4 Terminological Resources 50
3.4.1 Gene Ontology 50
3.4.2 Medical Subject Headings 53
3.4.3 UMLS Metathesaurus 54
3.5 Ontological Resources 55
3.5.1 SNOMEDCT 56
3.5-2 UMLS Semantic Network 56
3.5.3 Other Ontological Resources 58
3.6 Issues Related to Entity Recognition 58
3.6.1 Limited Coverage 59
3.6.2 Ambiguity 60
3.7 Issues Related to Relation Extraction 60
3.7.1 Terminological Versus Ontological Relations 60
Contents vii
3.7.2 Interactions Between Text Mining and
Terminological Resources 61
3.8 Conclusion 61
Acknowledgments 62
References 62
4 Automatic Terminology Management in Biomedicine 67
4.1 Introduction 67
4.1.1 Principles of Terminology 67
4.2 Terminological Resources in Biomedicine 70
4.3 Automatic Terminology Management 72
4.4 Automatic Term Recognition 73
4.4.1 Dictionary-Based Approaches 74
4.4.2 Rule-Based Approaches 75
4.4.3 Machine Learning Approaches 75
4.4.4 Statistical Approaches 76
4.4.5 Hybrid Approaches 77
4.4.6 Conclusion 78
4.5 Dealing with Term Variation and Ambiguity 78
4.5.1 Term Variations 78
4.5.2 Term Ambiguity 82
4.6 Automatic Term Structuring 83
4.7 Examples of Automatic Term Management Systems 86
4.8 Conclusion 90
References 92
5 Abbreviations in Biomedical Text 99
5.1 Introduction 99
5.2 Identifying Abbreviations 103
5.2.1 Heuristics 104
5.2.2 Alignment 105
5.2.3 Natural Language Processing 106
viii Text Mining for Biology and Biomedicine
5.2.4 Stanford Biomedical Abbreviation Method 106
5.2.5 Evaluating Abbreviation Identification Methods 109
5.3 Normalizing Abbreviations 112
5.4 Defining Abbreviations in Text 115
5.5 Abbreviation Databases 116
5.6 Conclusion 117
References 117
6 Named Entity Recognition 121
6.1 Introduction 121
6.2 Biomedical Named Entities 124
6.3 Issues in Gene/Protein Name Recognition 126
6.3.1 Ambiguous Names 126
6.3.2 Synonyms 127
6.3.3 Variations 127
6.3.4 Names of Newly Discovered Genes and Proteins 128
6.3.5 Varying Range of Target Names 129
6.4 Approaches to Gene and Protein Name Recognition 129
6.4.1 Dictionary-Based Approaches 130
6.4.2 Rule-Based Approaches 131
6.4.3 Machine Learning Approaches 132
6.4.4 Hybrid Approaches 134
6.4.5 Classification and Grounding of Biomedical
Named Entities 135
6.5 Discussion 136
6.6 Conclusion 138
References 138
7 Information Extraction 143
7.1 Information Extraction: The Task 143
7.1.1 Information Extraction and Information Retrieval 144
Contents ix
7.1.2 Information Extraction and Natural Language
Processing 145
7.2 The Message Understanding Conferences 146
7.2.1 Targets of MUC Analysis 146
7.3 Approaches to Information Extraction in Biology 148
7.3.1 Pattern-Matching Approaches 149
7.3.2 Basic Context Free Grammar Approaches 154
7.3.3 Full Parsing Approaches 154
7.3.4 Probability-Based Parsing 160
7.3.5 Mixed Syntax-Semantics Approaches 160
7.3.6 Sublanguage-Driven Information Extraction 163
7.3.7 Ontology-Driven Information Extraction 166
7.4 Conclusion 171
References 174
8 Corpora and Their Annotation 179
8.1 Introduction 179
8.2 Literature Databases in Biology 180
8.2.1 Literature Databases 180
8.2.2 Copyright Issues 181
8.3 Corpora 182
8.3.1 Corpora in Biology 182
8.3.2 Collecting MEDL1NE Abstracts 183
8.3.3 Comparing Corpora 184
8.4 Corpus Annotation in Biology 188
8.4.1 Annotation for Biomedical Entities 188
8.4.2 Annotation for Biological Processes 191
8.4.3 Annotation for Linguistic Structure 193
8.5 Issues on Manual Annotation 195
8.5.1 Quality Control 195
8.5.2 Format of Annotation 199
8.5.3 Discontinuous Expressions 202
8.6 Annotation Tools 203
x Text Mining for Biology and Biomedicine
8.6.1 Reuse of General Purpose Tools 204
8.6.2 Corpus Annotation Tools 208
8.7 Conclusion 209
Acknowledgments 209
References 209
9 Evaluation of Text Mining in Biology 213
9.1 Introduction 213
9.2 Why Evaluate? 216
9.2.1 The Stakeholders 216
9.2.2 Dimensions of a Successful Evaluation 217
9.2.3 What Can Evaluation Accomplish? 219
9.3 What to Evaluate? 220
9.3.1 Biological Applications 220
9.4 Current Assessments for Text Mining in Biology 223
9.4.1 KDD Challenge Cup 224
9.4.2 TREC Genomics Track 227
9.4.3 BioCreAtlvE 232
9.4.4 BioNLP 239
9.5 What Next? 240
References 243
10 Integrating Text Mining with Data Mining 247
10.1 Introduction: Biological Sequence Analysis and
Text Mining 247
10.1.1 Improving Homology Searches 250
10.1.2 Improving Sequence-Based Functional Classification 253
10.2 Gene Expression Analysis and Text Mining 256
10.2.1 Assigning Biological Explanations to Gene
Expression Clusters 258
10.2.2 Enhancing Expression Data Analysis with
Literature Knowledge 260
10.3 Conclusion 263
Contents xi
References 263
Acronyms 267
About the Authors 273
Index 277
|
| adam_txt |
Contents
1 Introduction 2
1.1 Text Mining: Aims, Challenges, and Solutions 1
1.2 Outline of the Book 7
Acknowledgments 11
Conventions 11
References 11
2 Levels of Natural Language Processing for
Text Mining 13
2.1 Introduction 13
2.2 The Lexical Level of Natural Language Processing 16
2.2.1 Tokenization 16
2.2.2 Morphological Analysis 18
2.2.3 Linguistic Lexicons 19
2.3 The Syntactic Level of Natural Language Processing 21
2.3.1 Part-of-Speech Tagging 22
2.3.2 Chunking 23
2.3.3 Parsing 25
2.4 The Semantic Level of Natural Language Processing 25
v
vi Text Mining for Biology and Biomedicine
2.4.1 Lexical Semantic Interpretation 25
2.4.2 Semantic Interpretation of Utterances 27
2.5 Natural Language System Architecture for
Text Mining 31
2.5.1 General Architecture 31
2.5.2 Two Concrete System Architectures 35
2.6 Conclusions and Outlook 36
References 38
3 Lexical, Terminological, and Ontological Resources
for Biological Text Mining 43
3.1 Introduction 43
3.2 Extended Example 45
3.2.1 Entity Recognition 45
3.2.2 Relation Extraction 46
3.3 Lexical Resources 48
3.3.1 WordNet 48
3.3.2 UMLS Specialist Lexicon 49
3.3.3 Other Specialized Resources 49
3.4 Terminological Resources 50
3.4.1 Gene Ontology 50
3.4.2 Medical Subject Headings 53
3.4.3 UMLS Metathesaurus 54
3.5 Ontological Resources 55
3.5.1 SNOMEDCT 56
3.5-2 UMLS Semantic Network 56
3.5.3 Other Ontological Resources 58
3.6 Issues Related to Entity Recognition 58
3.6.1 Limited Coverage 59
3.6.2 Ambiguity 60
3.7 Issues Related to Relation Extraction 60
3.7.1 Terminological Versus Ontological Relations 60
Contents vii
3.7.2 Interactions Between Text Mining and
Terminological Resources 61
3.8 Conclusion 61
Acknowledgments 62
References 62
4 Automatic Terminology Management in Biomedicine 67
4.1 Introduction 67
4.1.1 Principles of Terminology 67
4.2 Terminological Resources in Biomedicine 70
4.3 Automatic Terminology Management 72
4.4 Automatic Term Recognition 73
4.4.1 Dictionary-Based Approaches 74
4.4.2 Rule-Based Approaches 75
4.4.3 Machine Learning Approaches 75
4.4.4 Statistical Approaches 76
4.4.5 Hybrid Approaches 77
4.4.6 Conclusion 78
4.5 Dealing with Term Variation and Ambiguity 78
4.5.1 Term Variations 78
4.5.2 Term Ambiguity 82
4.6 Automatic Term Structuring 83
4.7 Examples of Automatic Term Management Systems 86
4.8 Conclusion 90
References 92
5 Abbreviations in Biomedical Text 99
5.1 Introduction 99
5.2 Identifying Abbreviations 103
5.2.1 Heuristics 104
5.2.2 Alignment 105
5.2.3 Natural Language Processing 106
viii Text Mining for Biology and Biomedicine
5.2.4 Stanford Biomedical Abbreviation Method 106
5.2.5 Evaluating Abbreviation Identification Methods 109
5.3 Normalizing Abbreviations 112
5.4 Defining Abbreviations in Text 115
5.5 Abbreviation Databases 116
5.6 Conclusion 117
References 117
6 Named Entity Recognition 121
6.1 Introduction 121
6.2 Biomedical Named Entities 124
6.3 Issues in Gene/Protein Name Recognition 126
6.3.1 Ambiguous Names 126
6.3.2 Synonyms 127
6.3.3 Variations 127
6.3.4 Names of Newly Discovered Genes and Proteins 128
6.3.5 Varying Range of Target Names 129
6.4 Approaches to Gene and Protein Name Recognition 129
6.4.1 Dictionary-Based Approaches 130
6.4.2 Rule-Based Approaches 131
6.4.3 Machine Learning Approaches 132
6.4.4 Hybrid Approaches 134
6.4.5 Classification and Grounding of Biomedical
Named Entities 135
6.5 Discussion 136
6.6 Conclusion 138
References 138
7 Information Extraction 143
7.1 Information Extraction: The Task 143
7.1.1 Information Extraction and Information Retrieval 144
Contents ix
7.1.2 Information Extraction and Natural Language
Processing 145
7.2 The Message Understanding Conferences 146
7.2.1 Targets of MUC Analysis 146
7.3 Approaches to Information Extraction in Biology 148
7.3.1 Pattern-Matching Approaches 149
7.3.2 Basic Context Free Grammar Approaches 154
7.3.3 Full Parsing Approaches 154
7.3.4 Probability-Based Parsing 160
7.3.5 Mixed Syntax-Semantics Approaches 160
7.3.6 Sublanguage-Driven Information Extraction 163
7.3.7 Ontology-Driven Information Extraction 166
7.4 Conclusion 171
References 174
8 Corpora and Their Annotation 179
8.1 Introduction 179
8.2 Literature Databases in Biology 180
8.2.1 Literature Databases 180
8.2.2 Copyright Issues 181
8.3 Corpora 182
8.3.1 Corpora in Biology 182
8.3.2 Collecting MEDL1NE Abstracts 183
8.3.3 Comparing Corpora 184
8.4 Corpus Annotation in Biology 188
8.4.1 Annotation for Biomedical Entities 188
8.4.2 Annotation for Biological Processes 191
8.4.3 Annotation for Linguistic Structure 193
8.5 Issues on Manual Annotation 195
8.5.1 Quality Control 195
8.5.2 Format of Annotation 199
8.5.3 Discontinuous Expressions 202
8.6 Annotation Tools 203
x Text Mining for Biology and Biomedicine
8.6.1 Reuse of General Purpose Tools 204
8.6.2 Corpus Annotation Tools 208
8.7 Conclusion 209
Acknowledgments 209
References 209
9 Evaluation of Text Mining in Biology 213
9.1 Introduction 213
9.2 Why Evaluate? 216
9.2.1 The Stakeholders 216
9.2.2 Dimensions of a Successful Evaluation 217
9.2.3 What Can Evaluation Accomplish? 219
9.3 What to Evaluate? 220
9.3.1 Biological Applications 220
9.4 Current Assessments for Text Mining in Biology 223
9.4.1 KDD Challenge Cup 224
9.4.2 TREC Genomics Track 227
9.4.3 BioCreAtlvE 232
9.4.4 BioNLP 239
9.5 What Next? 240
References 243
10 Integrating Text Mining with Data Mining 247
10.1 Introduction: Biological Sequence Analysis and
Text Mining 247
10.1.1 Improving Homology Searches 250
10.1.2 Improving Sequence-Based Functional Classification 253
10.2 Gene Expression Analysis and Text Mining 256
10.2.1 Assigning Biological Explanations to Gene
Expression Clusters 258
10.2.2 Enhancing Expression Data Analysis with
Literature Knowledge 260
10.3 Conclusion 263
Contents xi
References 263
Acronyms 267
About the Authors 273
Index 277 |
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| publisher | Artech House |
| record_format | marc |
| spelling | Text mining for biology and biomedicine Sophia Ananiadou, John McNaught, editors Boston Artech House 2006 xi, 286 p. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Information Storage and Retrieval/trends Natural Language Processing Abstracting and Indexing/methods Biologie - Informatique Exploration de données (Informatique) Médecine - Informatique Datenverarbeitung Medizin Biology Data processing Medicine Data processing Data mining Biologie (DE-588)4006851-1 gnd rswk-swf Text Mining (DE-588)4728093-1 gnd rswk-swf Biomedizin (DE-588)4647152-2 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Biologie (DE-588)4006851-1 s Text Mining (DE-588)4728093-1 s DE-604 Biomedizin (DE-588)4647152-2 s Ananiadou, Sophia Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016657820&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Text mining for biology and biomedicine Information Storage and Retrieval/trends Natural Language Processing Abstracting and Indexing/methods Biologie - Informatique Exploration de données (Informatique) Médecine - Informatique Datenverarbeitung Medizin Biology Data processing Medicine Data processing Data mining Biologie (DE-588)4006851-1 gnd Text Mining (DE-588)4728093-1 gnd Biomedizin (DE-588)4647152-2 gnd |
| subject_GND | (DE-588)4006851-1 (DE-588)4728093-1 (DE-588)4647152-2 (DE-588)4143413-4 |
| title | Text mining for biology and biomedicine |
| title_auth | Text mining for biology and biomedicine |
| title_exact_search | Text mining for biology and biomedicine |
| title_exact_search_txtP | Text mining for biology and biomedicine |
| title_full | Text mining for biology and biomedicine Sophia Ananiadou, John McNaught, editors |
| title_fullStr | Text mining for biology and biomedicine Sophia Ananiadou, John McNaught, editors |
| title_full_unstemmed | Text mining for biology and biomedicine Sophia Ananiadou, John McNaught, editors |
| title_short | Text mining for biology and biomedicine |
| title_sort | text mining for biology and biomedicine |
| topic | Information Storage and Retrieval/trends Natural Language Processing Abstracting and Indexing/methods Biologie - Informatique Exploration de données (Informatique) Médecine - Informatique Datenverarbeitung Medizin Biology Data processing Medicine Data processing Data mining Biologie (DE-588)4006851-1 gnd Text Mining (DE-588)4728093-1 gnd Biomedizin (DE-588)4647152-2 gnd |
| topic_facet | Information Storage and Retrieval/trends Natural Language Processing Abstracting and Indexing/methods Biologie - Informatique Exploration de données (Informatique) Médecine - Informatique Datenverarbeitung Medizin Biology Data processing Medicine Data processing Data mining Biologie Text Mining Biomedizin Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016657820&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ananiadousophia textminingforbiologyandbiomedicine |