  We propose NEMO, a system for extracting organization names in the
affiliation and normalizing them to a canonical organization name. Our parsing
process involves multi-layered rule matching with multiple dictionaries. The
system achieves more than 98% f-score in extracting organization names. Our
process of normalization that involves clustering based on local sequence
alignment metrics and local learning based on finding connected components. A
high precision was also observed in normalization. NEMO is the missing link in
associating each biomedical paper and its authors to an organization name in
its canonical form and the Geopolitical location of the organization. This
research could potentially help in analyzing large social networks of
organizations for landscaping a particular topic, improving performance of
author disambiguation, adding weak links in the co-author network of authors,
augmenting NLM's MARS system for correcting errors in OCR output of affiliation
field, and automatically indexing the PubMed citations with the normalized
organization name and country. Our system is available as a graphical user
interface available for download along with this paper.
