English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose NEMO, a system for extracting organization names in theaffiliation and normalizing them to a canonical organization name. Our parsingprocess involves multi-layered rule matching with multiple dictionaries. Thesystem achieves more than 98% f-score in extracting organization names. Ourprocess of normalization that involves clustering based on local sequencealignment metrics and local learning based on finding connected components. Ahigh precision was also observed in normalization. NEMO is the missing link inassociating each biomedical paper and its authors to an organization name inits canonical form and the Geopolitical location of the organization. Thisresearch could potentially help in analyzing large social networks oforganizations for landscaping a particular topic, improving performance ofauthor disambiguation, adding weak links in the co-author network of authors,augmenting NLM's MARS system for correcting errors in OCR output of affiliationfield, and automatically indexing the PubMed citations with the normalizedorganization name and country. Our system is available as a graphical userinterface available for download along with this paper.

NEMO: Extraction and normalization of organization names from PubMed affiliation strings