Learning to annotate scientific publications

  • Authors:

  • Minlie Huang;Zhiyong Lu

  • Affiliations:

  • Tsinghua University;National Institutes of Health

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
  • Year:
  • 2010

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Abstract

Annotating scientific publications with keywords and phrases is of great importance to searching, indexing, and cataloging such documents. Unlike previous studies that focused on user-centric annotation, this paper presents our investigation of various annotation characteristics on service-centric annotation. Using a large number of publicly available annotated scientific publications, we characterized and compared the two different types of annotation processes. Furthermore, we developed an automatic approach of annotating scientific publications based on a machine learning algorithm and a set of novel features. When compared to other methods, our approach shows significantly improved performance. Experimental data sets and evaluation results are publicly available at the supplementary website.