Biomedical Natural Language Processing

Name: Biomedical Natural Language Processing | Fetal Cell Experiments from Cyberspace to China
Brand: John Benjamins Publishing Company
Price: 118.99 EUR
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Fetal Cell Experiments from Cyberspace to China

Kevin Bretonnel Cohen Dina Demner-Fushman(Autor*in)

John Benjamins Publishing Company

1. Auflage

Erschienen am 27. Februar 2014

XI, 160 Seiten

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Inhalt

Biomedical Natural Language Processing
Editorial page
Title page
LCC data
Acknowledgments
Table of contents
List of figures
1. Introduction to natural language processing
1.1 Some definitions
1.1.1 Computational linguistics
1.1.2 Natural language processing
1.1.3 Text mining
1.1.4 Usage of these definitions in practice
1.2 Levels of document and linguistic structure and their relationship to natural language processin
1.2.1 Document structure
1.2.2 Sentences
1.2.3 Tokens
1.2.4 Stems and lemmata
1.2.5 Part of speech
1.2.6 Syntactic structure
1.2.7 Semantics
2. Historical background
2.1 Early work in the medical domain
2.2 The emergence of the biological domain
2.3 Clinical text mining
2.4 Types of users of biomedical NLP systems
2.5 Resources and tools
US National Library of Medicine
MEDLINE database
Medical Subject Headings
PubMed
GENIA
PubMed Central International
2.6 Legal and ethical issues
2.7 Is biomedical natural language processing effective?
3. Named entity recognition
3.1 Overview
3.2 The crucial role of named entity recognition in BioNLP tasks
3.3 Why gene names are the way they are
3.4 An example of a rule-based gene NER system: KeX/PROPER
3.5 An example of a statistical disease NER system
3.6 Evaluation
4. Relation extraction
4.1 Introduction
4.1.1 Protein-protein interactions as an information extraction target
4.2 Binarity of most biomedical information extraction systems
4.3 Beyond simple binary relations
4.4 Rule-based systems
4.4.1 Co-occurrence
4.4.2 Example rule-based systems
4.4.3 Machine learning systems
4.5 Relations in clinical narrative
4.5.1 MedLEE
4.6 SemRep
4.6.1 NegEX
4.7 Evaluation
5. Information retrieval/document classification
5.1 Background
5.1.1 Growth in the biomedical literature
5.1.2 PubMed/MEDLINE
5.2 Issues
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
5.5 Full text
5.6 Image and figure search
5.7 Captions
5.7.1 Evaluation
6. Concept normalization
6.1 Gene normalization
6.1.1 The BioCreative definition of the gene normalization task
6.2 Building a successful gene normalization system
6.2.1 Coordination and ranges
6.2.2 An example system
6.3 Normalization and extraction of clinically pertinent terms
6.3.1 MetaMap UMLS mapping tools
7. Ontologies and computational lexical semantics
7.1 Unified Medical Language System (UMLS)
7.1.1 The Gene Ontology
7.2 Recognizing ontology terms in text
7.3 NLP for ontology quality assurance
7.4 Mapping, alignment, and linking of ontologies
8. Summarization
8.1 Medical summarization systems
8.1.1 Overview of medical summarization systems
8.1.2 A representative medical summarization system: Centrifuser
8.2 Genomics summarization systems
8.2.1 Sentence selection for protein-protein interactions
8.2.2 EntrezGene SUMMARY field generation
9. Question-answering
9.1 Principles
9.1.1 Question analysis and formal representation
9.1.1.1 Clinical questions
9.1.2 Formal representation of questions
9.1.3 Domain model-based question representation
9.1.3.1 Genomics and translational research questions
9.1.4 Answer retrieval
9.1.5 Answer extraction and generation
9.1.5.1 Reference answer formats for clinical questions
9.1.5.2 Entity-extraction approaches to answer generation
9.2 Applications
9.2.1 Question analysis and query formulation
9.2.2 Knowledge Extraction
9.2.2.1 Population Extractor
9.2.2.2 Problem Extractor
9.2.2.3 Intervention Extractor
9.2.2.4 Outcome Extractor
9.2.2.5 Clinical Task classification
9.2.2.6 Strength of Evidence classification
9.2.2.7 Document scoring and ranking
9.2.3 Question-Document frame matching (PICO score)
9.2.3.1 Answer generation
9.2.4 Semantic clustering
Summary
10. Software engineering
10.1 Introduction
10.2 Principles
10.3 General software testing
10.3.1 Clean and dirty tests
10.3.2 Testing requires planning
10.3.3 Catalogues
10.3.4 How many tests are possible?
10.3.5 Equivalence classes
10.3.6 Boundary conditions
10.4 Code coverage
10.5 When your input is language
10.6 User interface evaluation
10.6.1 API interface usability
11. Corpus construction and annotation
11.1 Corpora in the two domains as driving forces of research
11.2 Who should build biomedical corpora?
11.3 The relationship between annotation of entities and annotation of linguistic structure
11.4 Commonly used biomedical corpora
11.4.1 GENIA
11.4.2 CRAFT
11.4.3 BioCreative gene mention corpora
11.4.4 AIMed
11.4.5 Word sense disambiguation
11.4.6 Clinical corpora
11.4.6.1 NLP Challenge
11.4.6.2 The MIMIC collection
11.5 Factors that contribute to the success of biomedical corpora
References
Index

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