Frontiers of biomedical text mining: current progress.
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.
Zweigenbaum
Pierre
P
LIMSI-CNRS, BP 133, 91403 Orsay Cedex, France. pz@limsi.fr
Demner-Fushman
Dina
D
Yu
Hong
H
Cohen
Kevin B
KB
D000043:Abstracting and Indexing / Q000639:trends*; D001185:Artificial Intelligence; D001695:Biology / Q000639:trends*; D016206:Databases, Bibliographic / Q000639:trends*; D005544:Forecasting; D009323:Natural Language Processing; D010506:Periodicals as Topic; D018875:Vocabulary, Controlled