ONER: Tool for Organization Named Entity Recognition from Affiliation Strings in PubMed Abstracts

Automatically extracting organization names from the affiliation sentences of articles related to biomedicine is of great interest to the pharmaceutical marketing industry, health care funding agencies and public health officials. It will also be useful for other scientists in normalizing author nam...

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Bibliographic Details
Published inarXiv.org
Main Authors Jonnalagadda, Siddhartha, Topham, Philip, Gonzalez, Graciela
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.03.2010
Subjects
Government agencies
Multilayers
Names
Normalizing
Public health
Sentences
Strings
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Summary:Automatically extracting organization names from the affiliation sentences of articles related to biomedicine is of great interest to the pharmaceutical marketing industry, health care funding agencies and public health officials. It will also be useful for other scientists in normalizing author names, automatically creating citations, indexing articles and identifying potential resources or collaborators. Today there are more than 18 million articles related to biomedical research indexed in PubMed, and information derived from them could be used effectively to save the great amount of time and resources spent by government agencies in understanding the scientific landscape, including key opinion leaders and centers of excellence. Our process for extracting organization names involves multi-layered rule matching with multiple dictionaries. The system achieves 99.6% f-measure in extracting organization names.
ISSN:2331-8422