Chapter
2.2 The emergence of the biological domain
2.2 The emergence of the biological domain
2.4 Types of users of biomedical NLP systems
2.4 Types of users of biomedical NLP systems
US National Library of Medicine
US National Library of Medicine
PubMed Central International
PubMed Central International
2.6 Legal and ethical issues
2.6 Legal and ethical issues
2.7 Is biomedical natural language processing effective?
2.7 Is biomedical natural language processing effective?
3. Named entity recognition
3. Named entity recognition
3.2 The crucial role of named entity recognition in BioNLP tasks
3.2 The crucial role of named entity recognition in BioNLP tasks
3.3 Why gene names are the way they are
3.3 Why gene names are the way they are
3.4 An example of a rule-based gene NER system: KeX/PROPER
3.4 An example of a rule-based gene NER system: KeX/PROPER
3.5 An example of a statistical disease NER system
3.5 An example of a statistical disease NER system
4.1.1 Protein-protein interactions as an information extraction target
4.1.1 Protein-protein interactions as an information extraction target
4.2 Binarity of most biomedical information extraction systems
4.2 Binarity of most biomedical information extraction systems
4.3 Beyond simple binary relations
4.3 Beyond simple binary relations
4.4.2 Example rule-based systems
4.4.2 Example rule-based systems
4.4.3 Machine learning systems
4.4.3 Machine learning systems
4.5 Relations in clinical narrative
4.5 Relations in clinical narrative
5. Information retrieval/document classification
5. Information retrieval/document classification
5.1.1 Growth in the biomedical literature
5.1.1 Growth in the biomedical literature
5.3 A knowledge-based system that disambiguates gene names
5.3 A knowledge-based system that disambiguates gene names
5.4 A phrase-based search engine, with term and concept expansion and probabilistic relevance rankin
5.4 A phrase-based search engine, with term and concept expansion and probabilistic relevance rankin
5.6 Image and figure search
5.6 Image and figure search
6.1.1 The BioCreative definition of the gene normalization task
6.1.1 The BioCreative definition of the gene normalization task
6.2 Building a successful gene normalization system
6.2 Building a successful gene normalization system
6.2.1 Coordination and ranges
6.2.1 Coordination and ranges
6.3 Normalization and extraction of clinically pertinent terms
6.3 Normalization and extraction of clinically pertinent terms
6.3.1 MetaMap UMLS mapping tools
6.3.1 MetaMap UMLS mapping tools
7. Ontologies and computational lexical semantics
7. Ontologies and computational lexical semantics
7.1 Unified Medical Language System (UMLS)
7.1 Unified Medical Language System (UMLS)
7.2 Recognizing ontology terms in text
7.2 Recognizing ontology terms in text
7.3 NLP for ontology quality assurance
7.3 NLP for ontology quality assurance
7.4 Mapping, alignment, and linking of ontologies
7.4 Mapping, alignment, and linking of ontologies
8.1 Medical summarization systems
8.1 Medical summarization systems
8.1.1 Overview of medical summarization systems
8.1.1 Overview of medical summarization systems
8.1.2 A representative medical summarization system: Centrifuser
8.1.2 A representative medical summarization system: Centrifuser
8.2 Genomics summarization systems
8.2 Genomics summarization systems
8.2.1 Sentence selection for protein-protein interactions
8.2.1 Sentence selection for protein-protein interactions
8.2.2 EntrezGene SUMMARY field generation
8.2.2 EntrezGene SUMMARY field generation
9.1.1 Question analysis and formal representation
9.1.1 Question analysis and formal representation
9.1.1.1 Clinical questions
9.1.1.1 Clinical questions
9.1.2 Formal representation of questions
9.1.2 Formal representation of questions
9.1.3 Domain model-based question representation
9.1.3 Domain model-based question representation
9.1.3.1 Genomics and translational research questions
9.1.3.1 Genomics and translational research questions
9.1.5 Answer extraction and generation
9.1.5 Answer extraction and generation
9.1.5.1 Reference answer formats for clinical questions
9.1.5.1 Reference answer formats for clinical questions
9.1.5.2 Entity-extraction approaches to answer generation
9.1.5.2 Entity-extraction approaches to answer generation
9.2.1 Question analysis and query formulation
9.2.1 Question analysis and query formulation
9.2.2 Knowledge Extraction
9.2.2 Knowledge Extraction
9.2.2.1 Population Extractor
9.2.2.1 Population Extractor
9.2.2.2 Problem Extractor
9.2.2.2 Problem Extractor
9.2.2.3 Intervention Extractor
9.2.2.3 Intervention Extractor
9.2.2.4 Outcome Extractor
9.2.2.4 Outcome Extractor
9.2.2.5 Clinical Task classification
9.2.2.5 Clinical Task classification
9.2.2.6 Strength of Evidence classification
9.2.2.6 Strength of Evidence classification
9.2.2.7 Document scoring and ranking
9.2.2.7 Document scoring and ranking
9.2.3 Question-Document frame matching (PICO score)
9.2.3 Question-Document frame matching (PICO score)
9.2.3.1 Answer generation
9.2.3.1 Answer generation
9.2.4 Semantic clustering
9.2.4 Semantic clustering
10.3 General software testing
10.3 General software testing
10.3.1 Clean and dirty tests
10.3.1 Clean and dirty tests
10.3.2 Testing requires planning
10.3.2 Testing requires planning
10.3.4 How many tests are possible?
10.3.4 How many tests are possible?
10.3.5 Equivalence classes
10.3.5 Equivalence classes
10.3.6 Boundary conditions
10.3.6 Boundary conditions
10.5 When your input is language
10.5 When your input is language
10.6 User interface evaluation
10.6 User interface evaluation
10.6.1 API interface usability
10.6.1 API interface usability
11. Corpus construction and annotation
11. Corpus construction and annotation
11.1 Corpora in the two domains as driving forces of research
11.1 Corpora in the two domains as driving forces of research
11.2 Who should build biomedical corpora?
11.2 Who should build biomedical corpora?
11.3 The relationship between annotation of entities and annotation of linguistic structure
11.3 The relationship between annotation of entities and annotation of linguistic structure
11.4 Commonly used biomedical corpora
11.4 Commonly used biomedical corpora
11.4.3 BioCreative gene mention corpora
11.4.3 BioCreative gene mention corpora
11.4.5 Word sense disambiguation
11.4.5 Word sense disambiguation
11.4.6.2 The MIMIC collection
11.4.6.2 The MIMIC collection
11.5 Factors that contribute to the success of biomedical corpora
11.5 Factors that contribute to the success of biomedical corpora
Biomedical Natural Language Processing
( Natural Language Processing )
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