Dina Vishnyakova1, Raul Rodriguez-Esteban1, Fabio Rinaldi2,3,4. 1. Roche Pharmaceutical Research and Early Development, pRED Informatics, Roche Innovation Center, Basel, Switzerland. 2. Institute of Computational Linguistics, University of Zurich, Switzerland. 3. Swiss Institute of Bioinformatics, Zurich, Switzerland. 4. Dalle Molle Institute for Artificial Intelligence Research (IDSIA), Manno, Switzerland.
Abstract
OBJECTIVE: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation. MATERIALS AND METHODS: Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards. RESULTS: We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms. DISCUSSION AND CONCLUSION: An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE. The gold standard and code used for this study are available at the following repository: https://github.com/amorgani/AND/.
OBJECTIVE: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation. MATERIALS AND METHODS: Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards. RESULTS: We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms. DISCUSSION AND CONCLUSION: An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE. The gold standard and code used for this study are available at the following repository: https://github.com/amorgani/AND/.
Authors: Wanli Liu; Rezarta Islamaj Doğan; Sun Kim; Donald C Comeau; Won Kim; Lana Yeganova; Zhiyong Lu; W John Wilbur Journal: J Assoc Inf Sci Technol Date: 2013-11-21 Impact factor: 2.687
Authors: Paul J Albert; Sarbajit Dutta; Jie Lin; Zimeng Zhu; Michael Bales; Stephen B Johnson; Mohammad Mansour; Drew Wright; Terrie R Wheeler; Curtis L Cole Journal: PLoS One Date: 2021-04-01 Impact factor: 3.240