Genomics Inform.  2019 Jun;17(2):e19. 10.5808/GI.2019.17.2.e19.

Towards cross-platform interoperability for machine-assisted text annotation

  • 1UKP Lab, Technical University Darmstadt, 64289 Darmstadt, Germany.
  • 2Vassar College, Poughkeepsie, NY 12604-0520, USA.
  • 3Database Center for Life Science, Research Organization of Information and Systems, Kashiwa 277-0871, Japan.


In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.


annotation software; biomedical text mining; interoperability

MeSH Terms

Machine Learning
Natural Language Processing
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