title: An entity tagger for recognizing acquired genomic variations in cancer literature.
abstract: VTag is an application for identifying the type, genomic location and genomic state-change of acquired genomic aberrations described in text. The application uses a machine learning technique called conditional random fields. VTag was tested with 345 training and 200 evaluation documents pertaining to cancer genetics. Our experiments resulted in 0.8541 precision, 0.7870 recall and 0.8192 F-measure on the evaluation set.
authors.affiliation: Department of Computer and Information Science, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104, USA.
authors.forename: Ryan T
authors.initials: RT
authors.lastname: McDonald
authors.forename: R Scott
authors.initials: RS
authors.lastname: Winters
authors.forename: Mark
authors.initials: M
authors.lastname: Mandel
authors.forename: Yang
authors.initials: Y
authors.lastname: Jin
authors.forename: Peter S
authors.initials: PS
authors.lastname: White
authors.forename: Fernando
authors.initials: F
authors.lastname: Pereira
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D016206:Databases, Bibliographic; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation; D016678:Genome; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D009369:Neoplasms / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D012984:Software; D014584:User-Computer Interface; D018875:Vocabulary, Controlled