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E-raamat: Biomedical Natural Language Processing

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(University of Colorado, School of Medicine), (National Library of Medicine)
  • Formaat: 171 pages
  • Sari: Natural Language Processing 11
  • Ilmumisaeg: 27-Feb-2014
  • Kirjastus: John Benjamins Publishing Co
  • Keel: eng
  • ISBN-13: 9789027271068
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Biomedical Natural Language ProcessingSuurem pilt
  • Formaat: 171 pages
  • Sari: Natural Language Processing 11
  • Ilmumisaeg: 27-Feb-2014
  • Kirjastus: John Benjamins Publishing Co
  • Keel: eng
  • ISBN-13: 9789027271068
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Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining.
List of figures xi
Chapter 1 Introduction to natural language processing 1(6)
1.1 Some definitions
1(1)
1.1.1 Computational linguistics
1(1)
1.1.2 Natural language processing
1(1)
1.1.3 Text mining
2(1)
1.1.4 Usage of these definitions in practice
2(1)
1.2 Levels of document and linguistic structure and their relationship to natural language processing
2(5)
1.2.1 Document structure
2(2)
1.2.2 Sentences
4(1)
1.2.3 Tokens
4(1)
1.2.4 Stems and lemmata
5(1)
1.2.5 Part of speech
5(1)
1.2.6 Syntactic structure
6(1)
1.2.7 Semantics
6(1)
Chapter 2 Historical background 7(14)
2.1 Early work in the medical domain
7(2)
2.2 The emergence of the biological domain
9(1)
2.3 Clinical text mining
10(1)
2.4 Types of users of biomedical NLP systems
11(1)
2.5 Resources and tools
12(5)
2.6 Legal and ethical issues
17(1)
2.7 Is biomedical natural language processing effective?
18(3)
Chapter 3 Named entity recognition 21(10)
3.1 Overview
21(1)
3.2 The crucial role of named entity recognition in BioNLP tasks
22(1)
3.3 Why gene names are the way they are
22(3)
3.4 An example of a rule-based gene NER system: KeX/PROPER
25(3)
3.5 An example of a statistical disease NER system
28(1)
3.6 Evaluation
29(2)
Chapter 4 Relation extraction 31(20)
4.1 Introduction
31(2)
4.1.1 Protein-protein interactions as an information extraction target
31(2)
4.2 Binarity of most biomedical information extraction systems
33(1)
4.3 Beyond simple binary relations
33(4)
4.4 Rule-based systems
37(6)
4.4.1 Co-occurrence
38(1)
4.4.2 Example rule-based systems
39(2)
4.4.3 Machine learning systems
41(2)
4.5 Relations in clinical narrative
43(2)
4.5.1 MedLEE
44(1)
4.6 SemRep
45(4)
4.6.1 NegEX
48(1)
4.7 Evaluation
49(2)
Chapter 5 Information retrieval/document classification 51(12)
5.1 Background
51(3)
5.1.1 Growth in the biomedical literature
51(1)
5.1.2 PubMed/MEDLINE
52(2)
5.2 Issues
54(1)
5.3 A knowledge-based system that disambiguates gene names
55(3)
5.4 A phrase-based search engine, with term and concept expansion and probabilistic relevance ranking
58(1)
5.5 Full text
59(1)
5.6 Image and figure search
60(1)
5.7 Captions
61(2)
5.7.1 Evaluation
61(2)
Chapter 6 Concept normalization 63(14)
6.1 Gene normalization
63(2)
6.1.1 The BioCreative definition of the gene normalization task
64(1)
6.2 Building a successful gene normalization system
65(6)
6.2.1 Coordination and ranges
66(1)
6.2.2 An example system
67(4)
6.3 Normalization and extraction of clinically pertinent terms
71(6)
6.3.1 MetaMap UMLS mapping tools
71(6)
Chapter 7 Ontologies and computational lexical semantics 77(10)
7.1 Unified Medical Language System (UMLS)
77(3)
7.1.1 The Gene Ontology
80(1)
7.2 Recognizing ontology terms in text
80(1)
7.3 NLP for ontology quality assurance
81(1)
7.4 Mapping, alignment, and linking of ontologies
82(5)
Chapter 8 Summarization 87(8)
8.1 Medical summarization systems
87(3)
8.1.1 Overview of medical summarization systems
87(1)
8.1.2 A representative medical summarization system: Centrifuser
88(2)
8.2 Genomics summarization systems
90(5)
8.2.1 Sentence selection for protein-protein interactions
93(1)
8.2.2 EntrezGene SUMMARY field generation
94(1)
Chapter 9 Question-answering 95(22)
9.1 Principles
95(8)
9.1.1 Question analysis and formal representation
95(1)
9.1.1.1 Clinical questions
95(1)
9.1.2 Formal representation of questions
96(1)
9.1.3 Domain model-based question representation
97(3)
9.1.3.1 Genomics and translational research questions
99(1)
9.1.4 Answer retrieval
100(1)
9.1.5 Answer extraction and generation
101(2)
9.1.5.1 Reference answer formats for clinical questions
101(1)
9.1.5.2 Entity-extraction approaches to answer generation
102(1)
9.2 Applications
103(14)
9.2.1 Question analysis and query formulation
104(1)
9.2.2 Knowledge Extraction
105(8)
9.2.2.1 Population Extractor
105(1)
9.2.2.2 Problem Extractor
106(1)
9.2.2.3 Intervention Extractor
106(1)
9.2.2.4 Outcome Extractor
107(1)
9.2.2.5 Clinical Task classification
108(3)
9.2.2.6 Strength of Evidence classification
111(1)
9.2.2.7 Document scoring and ranking
112(1)
9.2.3 Question-Document frame matching (PICO score)
113(2)
9.2.3.1 Answer generation
115(1)
9.2.4 Semantic clustering
115(2)
Chapter 10 Software engineering 117(14)
10.1 Introduction
117(1)
10.2 Principles
118(1)
10.3 General software testing
118(5)
10.3.1 Clean and dirty tests
119(1)
10.3.2 Testing requires planning
119(1)
10.3.3 Catalogues
120(1)
10.3.3.1 Answers to the exercise
120(1)
10.3.4 How many tests are possible?
120(1)
10.3.5 Equivalence classes
121(2)
10.3.6 Boundary conditions
123(1)
10.4 Code coverage
123(2)
10.5 When your input is language
125(2)
10.6 User interface evaluation
127(4)
10.6.1 API interface usability
128(3)
Chapter 11 Corpus construction and annotation 131(10)
11.1 Corpora in the two domains as driving forces of research
131(1)
11.2 Who should build biomedical corpora?
131(1)
11.3 The relationship between annotation of entities and annotation of linguistic structure
132(1)
11.4 Commonly used biomedical corpora
133(5)
11.4.1 GENIA
133(1)
11.4.2 CRAFT
134(1)
11.4.3 BioCreative gene mention corpora
135(1)
11.4.4 AIMed
136(1)
11.4.5 Word sense disambiguation
136(1)
11.4.6 Clinical corpora
136(2)
11.4.6.1 NLP Challenge
137(1)
11.4.6.2 The MIMIC collection
137(1)
11.5 Factors that contribute to the success of biomedical corpora
138(3)
References 141(14)
Index 155
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