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Abstract
The volume of published biomedical research, and therefore the underlying biomedical
knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers
in coping with this information overload are text mining and knowledge extraction.
Significant progress has been made in applying text mining to named entity recognition,
text classification, terminology extraction, relationship extraction and hypothesis
generation. Several research groups are constructing integrated flexible text-mining
systems intended for multiple uses. The major challenge of biomedical text mining
over the next 5-10 years is to make these systems useful to biomedical researchers.
This will require enhanced access to full text, better understanding of the feature
space of biomedical literature, better methods for measuring the usefulness of systems
to users, and continued cooperation with the biomedical research community to ensure
that their needs are addressed.