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Text Mining for Biology and BiomedicineFrom Brand: Artech House
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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 bioinformatics 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.
- Sales Rank: #900562 in Books
- Brand: Brand: Artech House
- Published on: 2006-01-27
- Original language: English
- Number of items: 1
- Dimensions: 9.34" h x .81" w x 6.48" l, 1.16 pounds
- Binding: Hardcover
- 286 pages
- Used Book in Good Condition
About the Author
Sophia Ananiadou is deputy director of the National Centre for Text Mining and reader in computer science at the University of Salford, Manchester, England. She received her Ph.D. in computational linguistics at the University of Manchester. John McNaught is associate director of the National Centre for Text Mining and a lecturer in informatics at the University of Manchester.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Text Analysis Explained to Biologists
By John M. Ford
This book was written to help biologists manage the flood of literature in their profession. Online search tools help some, but not enough. Text analysis, "the process of discovering and extracting knowledge from unstructured data," can help with this literature tsunami. Biologists--and researchers in related disciplines--can use the tools and techniques of text analysis to identify relevant documents, extract useful information from them, and discover new relationships between these hard-won nuggets.
Sophe Ananiadou and John McNaught have assembled a collection of chapters written by experienced text analysts. They cover the primary subareas of text analysis with little overlap. These chapters are well-documented, use examples from biology, and explain them well enough to make the book accessible to non-biologists. After a concise introductory outline in the first chapter, readers become familiar with the general techniques of natural language processing, the various research communities using NLP techniques, and the tools and other resources they have created. Subsequent chapters cover terminology management, abbreviations, named entities (think "proper nouns"), information extraction, and corpus tagging. The final two chapters discuss how text analysis techniques are evaluated and how results are integrated into structured data analysis.
The clear explanations and accessible examples make this book a reasonable first read for anyone curious about text analysis. Although readers need not be biologists, some familiarity with scientific inquiry is helpful. I found the chapter on evaluation of text analysis methods most useful. It explains the role of challenge competitions in advancing the field and describes the common evaluation metrics well enough to facilitate understanding of the research literature. All chapters include numerous web links to software tools, data sources, and relevant research groups. This is a particular strength of the book, and helps readers bridge the gap between text analysis as it was in 2005, and as it has become since the book's publication.
It is recommended for scientists who want to learn about text analysis by observing it's practical applications in biology. Readers preferring a "straight" introduction without the biology might turn to The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data or the last chapter in Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management.
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