Text mining for biology and biomedicine

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Text mining for biology and biomedicine / Sophia Ananiadou, John McNaught, editors

資料種別:

図書

出版情報:

Boston : Artech House, c2006

形態:

xi, 286 p. ; 24 cm

著者名:

ISBN:

9781580539845 [158053984X]

書誌ID:

BA80341356

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

注記:

Includes bibliographical references and index

主題:

Biology--Terminology--Data processing; Data mining; Keyword searching; Medicine--Terminology--Data processing

言語:

英語

目次情報:

Introduction / 1：

Text Mining: Aims, Challenges, and Solutions / 1.1：

Outline of the Book / 1.2：

Acknowledgments

Conventions

References

Levels of Natural Language Processing for Text Mining / 2：

The Lexical Level of Natural Language Processing / 2.1：

Tokenization / 2.2.1：

Morphological Analysis / 2.2.2：

Linguistic Lexicons / 2.2.3：

The Syntactic Level of Natural Language Processing / 2.3：

Part-of-Speech Tagging / 2.3.1：

Chunking / 2.3.2：

Parsing / 2.3.3：

The Semantic Level of Natural Language Processing / 2.4：

Lexical Semantic Interpretation / 2.4.1：

Semantic Interpretation of Utterances / 2.4.2：

Natural Language System Architecture for Text Mining / 2.5：

General Architecture / 2.5.1：

Two Concrete System Architectures / 2.5.2：

Conclusions and Outlook / 2.6：

Lexical, Terminological, and Ontological Resources for Biological Text Mining / 3：

Extended Example / 3.1：

Entity Recognition / 3.2.1：

Relation Extraction / 3.2.2：

Lexical Resources / 3.3：

WordNet / 3.3.1：

UMLS Specialist Lexicon / 3.3.2：

Other Specialized Resources / 3.3.3：

Terminological Resources / 3.4：

Gene Ontology / 3.4.1：

Medical Subject Headings / 3.4.2：

UMLS Metathesaurus / 3.4.3：

Ontological Resources / 3.5：

SNOMED CT / 3.5.1：

UMLS Semantic Network / 3.5.2：

Other Ontological Resources / 3.5.3：

Issues Related to Entity Recognition / 3.6：

Limited Coverage / 3.6.1：

Ambiguity / 3.6.2：

Issues Related to Relation Extraction / 3.7：

Terminological Versus Ontological Relations / 3.7.1：

Interactions Between Text Mining and Terminological Resources / 3.7.2：

Conclusion / 3.8：

Automatic Terminology Management in Biomedicine / 4：

Principles of Terminology / 4.1：

Terminological Resources in Biomedicine / 4.2：

Automatic Terminology Management / 4.3：

Automatic Term Recognition / 4.4：

Dictionary-Based Approaches / 4.4.1：

Rule-Based Approaches / 4.4.2：

Machine Learning Approaches / 4.4.3：

Statistical Approaches / 4.4.4：

Hybrid Approaches / 4.4.5：

Dealing with Term Variation and Ambiguity / 4.4.6：

Term Variations / 4.5.1：

Term Ambiguity / 4.5.2：

Automatic Term Structuring / 4.6：

Examples of Automatic Term Management Systems / 4.7：

Abbreviations in Biomedical Text / 4.8：

Identifying Abbreviations / 5.1：

Heuristics / 5.2.1：

Alignment / 5.2.2：

Natural Language Processing / 5.2.3：

Stanford Biomedical Abbreviation Method / 5.2.4：

Evaluating Abbreviation Identification Methods / 5.2.5：

Normalizing Abbreviations / 5.3：

Defining Abbreviations in Text / 5.4：

Abbreviation Databases / 5.5：

Named Entity Recognition / 5.6：

Biomedical Named Entities / 6.1：

Issues in Gene/Protein Name Recognition / 6.3：

Ambiguous Names / 6.3.1：

Synonyms / 6.3.2：

Variations / 6.3.3：

Names of Newly Discovered Genes and Proteins / 6.3.4：

Varying Range of Target Names / 6.3.5：

Approaches to Gene and Protein Name Recognition / 6.4：

Classification and Grounding of Biomedical Named Entities / 6.4.1：

Discussion / 6.5：

Information Extraction / 6.6：

Information Extraction: The Task / 7.1：

Information Extraction and Information Retrieval / 7.1.1：

Information Extraction and Natural Language Processing / 7.1.2：

The Message Understanding Conferences / 7.2：

Targets of MUC Analysis / 7.2.1：

Approaches to Information Extraction in Biology / 7.3：

Pattern-Matching Approaches / 7.3.1：

Basic Context Free Grammar Approaches / 7.3.2：

Full Parsing Approaches / 7.3.3：

Probability-Based Parsing / 7.3.4：

Mixed Syntax-Semantics Approaches / 7.3.5：

Sublanguage-Driven Information Extraction / 7.3.6：

Ontology-Driven Information Extraction / 7.3.7：

Corpora and Their Annotation / 7.4：

Literature Databases in Biology / 8.1：

Literature Databases / 8.2.1：

Corpora / 8.3：

Corpora in Biology / 8.3.1：

Collecting MEDLINE Abstracts / 8.3.2：

Comparing Corpora / 8.3.3：

Corpus Annotation in Biology / 8.4：

Annotation for Biomedical Entities / 8.4.1：

Annotation for Biological Processes / 8.4.2：

Annotation for Linguistic Structure / 8.4.3：

Issues on Manual Annotation / 8.5：

Quality Control / 8.5.1：

Format of Annotation / 8.5.2：

Discontinuous Expressions / 8.5.3：

Annotation Tools / 8.6：

Reuse of General Purpose Tools / 8.6.1：

Corpus Annotation Tools / 8.6.2：

Evaluation of Text Mining in Biology / 8.7：

Why Evaluate? / 9.1：

The Stakeholders / 9.2.1：

Dimensions of a Successful Evaluation / 9.2.2：

What Can Evaluation Accomplish? / 9.2.3：

What to Evaluate? / 9.3：

Biological Applications / 9.3.1：

Current Assessments for Text Mining in Biology / 9.4：

KDD Challenge Cup / 9.4.1：

TREC Genomics Track / 9.4.2：

BioCreAtIvE / 9.4.3：

BioNLP / 9.4.4：

What Next? / 9.5：

Integrating Text Mining with Data Mining / 10：

Introduction: Biological Sequence Analysis and Text Mining / 10.1：

Improving Homology Searches / 10.1.1：

Improving Sequence-Based Functional Classification / 10.1.2：

Gene Expression Analysis and Text Mining / 10.2：

Assigning Biological Explanations to Gene Expression Clusters / 10.2.1：

Enhancing Expression Data Analysis with Literature Knowledge / 10.2.2：

Acronyms / 10.3：

About the Authors

Index

Introduction / 1：

Text Mining: Aims, Challenges, and Solutions / 1.1：

Outline of the Book / 1.2：

Acknowledgments

Conventions

References

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