Status of text-mining techniques applied to biomedical text.
Scientific progress is increasingly based on knowledge and information. Knowledge is now recognized as the driver of productivity and economic growth, leading to a new focus on the role of information in the decision-making process. Most scientific knowledge is registered in publications and other unstructured representations that make it difficult to use and to integrate the information with other sources (e.g. biological databases). Making a computer understand human language has proven to be a complex achievement, but there are techniques capable of detecting, distinguishing and extracting a limited number of different classes of facts. In the biomedical field, extracting information has specific problems: complex and ever-changing nomenclature (especially genes and proteins) and the limited representation of domain knowledge.
Bioalma, Ronda de Poniente 4, 28760 Tres Cantos, Madrid, Spain.
Ramón A-A
RA
Erhardt
Reinhard
R
Schneider
Christian
C
Blaschke
D000043:Abstracting and Indexing; D035843:Biomedical Research; D016206:Databases, Bibliographic; D004014:Dictionaries as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D007802:Language; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled