One tagger, many uses: Simple text-mining strategies for biomedicine

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The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.

Science

Lars Juhl Jensen
@larsjuhljensen
One tagger, many uses
Simple text-mining strategies for
biomedicine

open source
bitbucket.org/larsjuhljensen/tagger/

Docker
hub.docker.com/r/larsjuhljensen/tagger/

Jensen et al., Nature Reviews Genetics, 2012

Drug introduction Drug discontinuation
Adverse eventNegative modifier Indication Pre-existing
condition
Adverse drug reaction Possible
adverse drug reaction
Adverse event
ADR of
additional drug
Identification start
Eriksson et al., Drug Safety, 2014

Acknowledgments
Evangelos Pafilis
Sune Pletscher-
Frankild
Nadezhda
Doncheva
Damian Szklarczyk
Michael Kuhn
Robert Eriksson
Peter Bjødstrup
Jensen
John “Scooter”
Morris
Christian von
Mering
Peer Bork
Christos Arvanitidis
Søren Brunak

One tagger, many uses: Simple text-mining strategies for biomedicine

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