Text Mining for Biology and Biomedicine
Packed with field-tested expertise and advice, this book describes the methods of natural language processing and their applications in the biological domain.
TABLE OF CONTENTS Text Mining for Biology and Biomedicine
Chapter 4: Automatic Terminology Management in Biomedicine
Sophia Ananiadou Goran Nenadic
4.1 Introduction
The dynamic and rapidly evolving field of biomedicine makes the processing and management of terminology one of the key factors for accessing the information stored in literature, since information across scientific articles is conveyed through terms and their relationships. Without knowledge of the terminology of a domain, the understanding of scientific documents is hindered. In this chapter, we examine how text mining can facilitate terminology management in biomedicine. We examine the basic concepts of term and terminology, the relation between concepts and terms, and issues related with term variation and term ambiguity. We also present approaches for automatically recognizing and structuring terminology from literature, and for discovering associations between terms that can be used for terminology management.
4.1.1 Principles of Terminology
Any domain is organized around a network of concepts and associations between these concepts. We define "term" as the linguistic realization of a specialized concept in a given domain. For example, biomedical terms refer both to concepts, such as gene, protein, disease, interaction, and inhibition; as well as to names, such as gene products, organisms, drugs, chemical compounds, and so forth. The main purpose of terms, as opposed to words, is the classification of specialized knowledge: "The classification principle [ ] becomes the chief motivation in designation" [1] Terms are means of scientific communication and are used by specialists to convey knowledge within a domain. They delineate the knowledge space of a domain.
We refer to the collection of terms belonging to a...
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