The results of the comparison of 10 open-source bibliographic reference parsers

Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers

Our paper “Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the Read more…

The workflow of author contributions extraction

Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes

Our paper “Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Creating scientific publications is a complex process. It is composed of a number of different activities, such as designing the experiments, Read more…

Trinity College Dublin, Home of our Recommender-Systems Research

Various positions to work on research-paper recommender systems (Mr. DLib) and Docear (Bachelor/Master/PhD/Post-Doc)

Update on 2018-03-15: We Are Hiring 1 Software Engineer & 1 Software Architect / Product Owner for a Recommender-System Business Start-up   Updated on 2017-08-14: Here at Docear and Mr. DLib we have many exciting projects in the field of recommender systems, user modelling, personalisation, and adaptive systems (primarily with Read more…