title: Unsupervised gene function extraction using semantic vectors.
abstract: Finding gene functions discussed in the literature is an important task of information extraction (IE) from biomedical documents. Automated computational methodologies can significantly reduce the need for manual curation and improve quality of other related IE systems. We propose an open-IE method for the BioCreative IV GO shared task (subtask b), focused on finding gene function terms [Gene Ontology (GO) terms] for different genes in an article. The proposed open-IE approach is based on distributional semantic similarity over the GO terms. The method does not require annotated data for training, which makes it highly generalizable. We achieve an F-measure of 0.26 on the test-set in the official submission for BioCreative-GO shared task, the third highest F-measure among the seven participants in the shared task.
  https://code.google.com/p/rainbow-nlp/
authors.affiliation: Department of Biomedical Informatics, Arizona State University, AZ 85259, USA eemadzad@asu.edu.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Emadzadeh
authors.affiliation: Department of Biomedical Informatics, Arizona State University, AZ 85259, USA.
authors.forename: Azadeh
authors.initials: A
authors.lastname: Nikfarjam
authors.affiliation: Department of Biomedical Informatics, Arizona State University, AZ 85259, USA.
authors.forename: Rachel E
authors.initials: RE
authors.lastname: Ginn
authors.affiliation: Department of Biomedical Informatics, Arizona State University, AZ 85259, USA.
authors.forename: Graciela
authors.initials: G
authors.lastname: Gonzalez
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D005796:Genes / Q000235:genetics / Q000502:physiology; D020407:Internet; D012660:Semantics; D018875:Vocabulary, Controlled