MOTIVATION: The extraction of sequence variants from the literature remains an 
important task. Existing methods primarily target standard (ST) mutation 
mentions (e.g. 'E6V'), leaving relevant mentions natural language (NL) largely 
untapped (e.g. 'glutamic acid was substituted by valine at residue 6').
RESULTS: We introduced three new corpora suggesting named-entity recognition 
(NER) to be more challenging than anticipated: 28-77% of all articles contained 
mentions only available in NL. Our new method nala captured NL and ST by 
combining conditional random fields with word embedding features learned 
unsupervised from the entire PubMed. In our hands, nala substantially 
outperformed the state-of-the-art. For instance, we compared all unique mentions 
in new discoveries correctly detected by any of three methods (SETH, tmVar, or 
nala ). Neither SETH nor tmVar discovered anything missed by nala , while nala 
uniquely tagged 33% mentions. For NL mentions the corresponding value shot up to 
100% nala -only.
AVAILABILITY AND IMPLEMENTATION: Source code, API and corpora freely available 
at: http://tagtog.net/-corpora/IDP4+ .
CONTACT: nala@rostlab.org.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.