Skip to search boxSkip to navigationSkip to main content

Effective piecewise CNN with attention mechanism for distant supervision on relation extraction task

  • Gangmin Li
    ,
  • Yuming Li
    ,
  • Pin Ni
    ,
  • Victor Chang
  • University of Liverpool
    ,
  • Teesside University
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

Abstract

Relation Extraction is an important sub-task in the field of information extraction. Its goal is to identify entities from text and extract semantic relationships between entities. However, the current Relationship Extraction task based on deep learning methods generally have practical problems such as insufficient amount of manually labeled data, so training under weak supervision has become a big challenge. Distant Supervision is a novel idea that can automatically annotate a large number of unlabeled data based on a small amount of labeled data. Based on this idea, this paper proposes a method combining the Piecewise Convolutional Neural Networks and Attention mechanism for automatically annotating the data of Relation Extraction task. The experiments proved that the proposed method achieved the highest precision is 76.24% on NYT-FB (New York Times-Freebase) dataset (top 100 relation categories). The results show that the proposed method performed better than CNN-based models in most cases.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 53-62

Publication milestones

  • Published - 09/05/2020

Publication status

Published - 09/05/2020

Publisher

SciTePress

ISBN (Print)

9789897584275

Publication IDs

  • handle.net: 10547/626425
  • Scopus: 85090380097

Host publication title

Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS

Access to documents

Publication metrics

Metrics

Download statistics
Download count
3