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Mining Relations from Unstructured Content

  • Ismini Lourentzou
  • Alfredo Alba
  • Anni Coden
  • Anna Lisa GentileEmail author
  • Daniel Gruhl
  • Steve Welch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10938)

Abstract

Extracting relations from unstructured Web content is a challenging task and for any new relation a significant effort is required to design, train and tune the extraction models. In this work, we investigate how to obtain suitable results for relation extraction with modest human efforts, relying on a dynamic active learning approach. We propose a method to reliably generate high quality training/test data for relation extraction - for any generic user-demonstrated relation, starting from a few user provided examples and extracting valuable samples from unstructured and unlabeled Web content. To this extent we propose a strategy which learns how to identify the best order to human-annotate data, maximizing learning performance early in the process. We demonstrate the viability of the approach (i) against state of the art datasets for relation extraction as well as (ii) a real case study identifying text expressing a causal relation between a drug and an adverse reaction from user generated Web content.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ismini Lourentzou
    • 1
  • Alfredo Alba
    • 3
  • Anni Coden
    • 2
  • Anna Lisa Gentile
    • 3
    Email author
  • Daniel Gruhl
    • 3
  • Steve Welch
    • 3
  1. 1.University of Illinois at Urbana - ChampaignChampaignUSA
  2. 2.IBM Watson Research LaboratoryNew YorkUSA
  3. 3.IBM Research AlmadenSan JoseUSA

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