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

TL;DR

ONER is a high-accuracy tool designed to extract organization names from biomedical affiliation strings in PubMed abstracts, aiding various scientific and administrative tasks.

Contribution

It introduces a multi-layered rule matching system with multiple dictionaries that achieves 99.6% f-measure in organization name extraction.

Findings

Achieves 99.6% f-measure in extracting organization names

Effective for biomedical affiliation string normalization

Supports applications in bibliometrics and public health

Abstract

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.