Selecting Documents Relevant for Chemistry as a Classification Problem

  • Zhemin ZhuEmail author
  • Saber A. Akhondi
  • Umesh Nandal
  • Marius Doornenbal
  • Michelle Gregory
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10180)


We present a first version of a system for selecting chemical publications for inclusion in a chemistry information database. This database, Reaxys (, is a portal for the retrieval of structured chemistry information from published journals and patents. There are three challenges in this task: (i) Training and input data are highly imbalanced; (ii) High recall (\({\ge }95\%\)) is desired; and (iii) Data offered for selection is numerically massive but at the same time, incomplete. Our system successfully handles the imbalance with the undersampling technique and achieves relatively high recall using chemical named entities as features. Experiments on a real-world data set consisting of 15,822 documents show that the features of chemical named entities boost recall by \(8\%\) over the usual n-gram features being widely used in general document classification applications. For fostering research on this challenging topic, a part of the data set compiled in this paper can be requested.


Natural language processing Document classification Machine learning Cheminfomatics 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zhemin Zhu
    • 1
    Email author
  • Saber A. Akhondi
    • 1
  • Umesh Nandal
    • 1
  • Marius Doornenbal
    • 1
  • Michelle Gregory
    • 1
  1. 1.ElsevierAmsterdamThe Netherlands

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