2006 TextMiningForBiologyAndBiomedicine
- (Ananiadou & McNaught, 2006) ⇒ Sophia Ananiadou, John McNaught. (2006). “Text Mining for Biology and Biomedicine." Artech House. ISBN 158053984X
Subject Headings: Text Mining, Biology, Biomedicine.
Notes
Cited By
2007
- (Witte et al., 2007) ⇒ René Witte, Thomas Kappler, and Christopher J. O. Baker. (2007). “Ontology Design for Biomedical Text Mining.” In: Book Chapter in: Semantic Web. doi:10.1007/978-0-387-48438-9
- (Zweigenbaum et al., 2007) ⇒ Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, and Kevin B. Cohen. (2007). “Frontiers of Biomedical Text Mining: current progress.” In: Briefings in Bioinformatics 2007, 8(5). doi:10.1093/bib/bbm045
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Book Overview
With the volume of biomedical research growing exponentially worldwide, the demand for information retrieval expertise in the field has never been greater. Here's the first guide for bionformatics practitioners that puts the full range of biological text mining tools and techniques at their fingertips in a single dedicated volume. It describes the methods of natural language processing (NLP) and their applications in the biological domain, and spells out the various lexical, terminological, and ontological resources at their disposal -- and how best to utilize them. Readers see how terminology management tools like term extraction and term structuring facilitate effective mining, and learn ways to readily identify biomedical named entities and abbreviations. The book explains how to deploy various information extraction methods for biological applications. It helps professionals evaluate and optimize text mining systems, and includes techniques for integrating text mining and data mining efforts to further facilitate biological analyses.
Here’s the first focused book that puts the full range of cutting-edge biological text mining techniques and tools at your command. This comprehensive volume describes the methods of natural language processing (NLP) and their applications in the biological domain, and spells out in detail the various lexical, terminological, and ontological resources now at your disposal — and how best to utilize them.
You see how terminology management tools like term extraction and term structuring facilitate effective mining, and learn ways to readily identify biomedical named entities and abbreviations. The book offers step-by-step guidance to implement various information extraction methods for biological applications, from pattern matching and full parsing approaches to sublanguage- and ontology-driven extraction techniques. It discusses strategies to make the most of text collections and to use corpora and corpus annotation efficiently in text mining applications, and also gives you tested guidelines for evaluating and optimizing text mining systems. Rounding out the volume are techniques for integrating text mining and data mining efforts to further facilitate biological analyses.
Both a critical review of the state of the art and a solution-focused guide packed with field-tested expertise and advice, this first-of-its-kind work will prove indispensable whether you’re long experienced with text mining from biomedical literature or entirely new to the field.
Table of Contents
- Introduction to Text Mining for Biology and Biomedicine
- Text Mining: Aims, Challenges and Solutions. Outline of the Book. References.
- Levels of Natural Language Processing for Text Mining
- Introduction. The Lexical Level of Natural Language Processing. The Syntactic Level of Natural Language Processing. The Semantic Level of Natural Language Processing. Natural Language System Architecture for Text Mining. Conclusions and Outlook. References.
- (Bodenreider, 2006) ⇒ Olivier Bodenreider. (2006). “Lexical, Terminological and Ontological Resources For Biological Text Mining.” In: (Ananiadou & McNaught, 2006).
- Introduction. Extended Example. Lexical Resources. Terminological Resources. Ontological Resources. Issues Related to Entity Recognition. Issues Related to Relation Extraction. Conclusion. References.
- Automatic Terminology Management in Biomedicine
- Introduction. Terminological Resources in Biomedicine. Automatic Terminology Management. Automatic Term Recognition. Dealing with Term Variation and Ambiguity. Automatic Term Structuring. Examples of Automatic Term Management Systems. Conclusion. References.
- (Chang & Schütze, 2006) ⇒ Jeffrey T. Chang, and Hinrich Schutze. (2006). “Abbreviations in Biomedical Text.” In: * (Ananiadou & McNaught, 2006).
- Introduction. Identifying Abbreviations. Normalizing Abbreviations. Defining Abbreviations in Text. Abbreviation Databases. Conclusions. References.
- It reports an estimate that 64,242 new abbreviations are introduced in 2004
- Named Entity Recognition
- Introduction. Biomedical Named Entities. Issues in Gene/Protein Name Recognition. Approaches to Gene and Protein Name Recognition. Discussion. Conclusion. References.
- Information Extraction
- Information Extraction: The Task. The Message Understanding Conferences. Approaches to Information Extraction in Biology. Conclusion. References.
- Corpora and their Annotation
- Introduction. Literature Databases in Biology. Corpora. Corpus Annotation in Biology. Issues on Manual Annotation. Annotation Tools. Conclusion.
- Evaluation of Text Mining in Biology
- Introduction. Why Evaluate? What to Evaluate? Current Assessments for Text Mining in Biology. What Next? References.
- Integrating Text Mining with Data Mining
- Introduction: Biological Sequence Analysis and Text Mining. Gene Expression Analysis and Text Mining. Conclusion. References.
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