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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Drug Discovery Todayarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Drug Discovery Today
Article . 2006 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Status of text-mining techniques applied to biomedical text

Authors: Ramón A-A, Erhardt; Reinhard, Schneider; Christian, Blaschke;

Status of text-mining techniques applied to biomedical text

Abstract

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.

Keywords

Biomedical Research, Abstracting and Indexing, Dictionaries as Topic, Information Storage and Retrieval, Databases, Bibliographic, Pattern Recognition, Automated, Semantics, Vocabulary, Controlled, Terminology as Topic, Humans, Periodicals as Topic, Language, Natural Language Processing

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    90
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
90
Top 10%
Top 1%
Top 10%
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