Leveraging Document-Specific Information for Classifying Relations in Scientific Articles

  • Qin DaiEmail author
  • Naoya Inoue
  • Paul Reisert
  • Kentaro Inui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10838)


Tremendous amount of knowledge is present in the ever-growing scientific literature. In order to grasp this massive amount knowledge, various computational tasks are proposed for training computers to read and analyze scientific documents. As one of these task, semantic relationship classification aims at automatically analyzing semantic relationships in scientific documents. Conventionally, only a limited number of commonly used knowledge bases such as Wikipedia are used for collecting background information for this task. In this work, we hypothesize that scientific papers also could be utilized as a source of background information for semantic relationship classification. Based on the hypothesis, we propose the model that is capable of extracting background information from unannotated scientific papers. Preliminary experiments on the RANIS dataset [1] proves the effectiveness of the proposed model on relationship classification in scientific articles.


Semantic relationship Scientific document Lexical chain 



This work was supported by JST CREST Grant Number JPMJCR1513, Japan and KAKENHI Grant Number 16H06614.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Qin Dai
    • 1
    Email author
  • Naoya Inoue
    • 1
  • Paul Reisert
    • 2
  • Kentaro Inui
    • 1
    • 2
  1. 1.Tohoku UniversitySendaiJapan
  2. 2.RIKEN Center for Advanced Intelligence ProjectWakoJapan

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