Abstract
Text processing systems now supplement the information needs of professionals across a variety of industries. Applications such as relationship extraction, information retrieval, document summarization, question answering, and multilingual machine translation demonstrate practical utility in terms of accuracy and speed. Significant drivers behind these advances stem from performance improvements in underlying technologies such as syntactic parsing, named entity recognition, and semantic interpretation. Text mining consolidates these and other language processing technologies to extract meaningful information. This chapter surveys the field of biomedical text mining and develops a case study to illustrate the underlying resources that are available, as well as the technologies that are commonly applied. The case study is designed to identify and extract relationships between genotypes, pathogens, and syndromes. Through the use of text processing it is possible to transform such relationships from disparate unstructured text sources into structured repositories. By identifying and organizing relationships that are scattered across diverse areas and literatures, it is possible to enhance our understanding of the complex machinery that drives biological processes.
| Original language | English |
|---|---|
| Title of host publication | Infectious disease informatics |
| Editors | Vitali Sintchenko |
| Place of Publication | New York |
| Publisher | Springer, Springer Nature |
| Pages | 149-165 |
| Number of pages | 17 |
| ISBN (Electronic) | 9781441913272 |
| ISBN (Print) | 9781441913265 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |