Leveraging unannotated texts for scientific relation extraction

Qin Dai, Naoya Inoue, Paul Reisert, Kentaro Inui

Research output: Contribution to journalArticlepeer-review

Abstract

A tremendous amount of knowledge is present in the ever-growing scientific literature. In order to efficiently grasp such knowledge, various computational tasks are proposed that train machines to read and analyze scientific documents. One of these tasks, Scientific Relation Extraction, aims at automatically capturing scientific semantic relationships among entities in scientific documents. Conventionally, only a limited number of commonly used knowledge bases, such as Wikipedia, are used as a source of background knowledge for relation extraction. In this work, we hypothesize that unannotated scientific papers could also be utilized as a source of external background information for relation extraction. Based on our hypothesis, we propose a model that is capable of extracting background information from unannotated scientific papers. Our experiments on the RANIS corpus [1] prove the effectiveness of the proposed model on relation extraction from scientific articles.

Original languageEnglish
Pages (from-to)3209-3217
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE101D
Issue number12
DOIs
Publication statusPublished - 2018 Dec

Keywords

  • Relation extraction
  • Scientific document
  • Semantically related word
  • Word embedding

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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