title: Ambiguity and variability of database and software names in bioinformatics.
abstract: BACKGROUND
  There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification.
  
  
  RESULTS
  Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature.
  
  
  CONCLUSIONS
  Our analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy.
authors.affiliation: School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK.
authors.forename: Geraint
authors.initials: G
authors.lastname: Duck
authors.affiliation: Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Kovacevic
authors.affiliation: Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PT UK.
authors.forename: David L
authors.initials: DL
authors.lastname: Robertson
authors.affiliation: School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Stevens
authors.affiliation: School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK ; Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN UK.
authors.forename: Goran
authors.initials: G
authors.lastname: Nenadic
keywords: Bioinformatics; CRF; Computational biology; Dictionary; Resource extraction; Text-mining