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.
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.
Introduction to Text Mining for Biology. Levels of Natural Language Processing for Text Mining. Lexical, Terminological and Ontological Resources For Biological Text Mining. Automatic Terminology Management in Biomedicine. Abbreviations in Biomedical Text. Named Entity Recognition. Information Extraction. Corpora and their Annotation. Evaluation of Text Mining in Biology. Integrating Text Mining with Data Mining.